Publish Swift1.5 Q5 with FP8 PLE

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,233 @@
+Swift Open License v1.0
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
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+END OF TERMS AND CONDITIONS
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+APPENDIX: Notice for redistributors (quantizations, conversions, merges).
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+   Copyright 2026 UkisAI. Swift Contribution licensed under the Swift Open
+   License v1.0 (https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/blob/main/LICENSE).
+   Derivative of Qwen3.8-Flash-Next, Copyright (c) 2026 Qwen, Qwen Community License 1.0.

LICENSE-QWEN

@@ -0,0 +1,16 @@
+Qwen Community License 1.0
+
+Copyright (c) 2026 Qwen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software, including the model weights, parameters, configuration files, inference code and associated documentation files (collectively, the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, sell, deploy, host, fine-tune, and create derivative works from (collectively, "Use" or "Using") copies of the Software; and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. If the Software (or any derivative works thereof) is Used for any of the licensee's commercial products or services that have more than 100,000,000 monthly active users or US$ 20,000,000 (or equivalent in other currencies) monthly revenue, respective model name must be prominently displayed on the user interface of such product or service; and,
+
+2. If the licensee or any of its affiliates conducts a Model as a Service or AI Work Assistant business, the licensee shall obtain a separate license from Qwen before Using the Software or its derivative works for any commercial purpose. The foregoing requirement shall not apply to the licensee's internal Use of the Software, provided that such Use does not make the Software, its outputs, or its underlying model capabilities available to any third party.
+
+"Model as a Service" means giving a third party access to language model inference or fine-tuning (e.g., via API or a hosted endpoint) in a manner that allows such third parties to exercise meaningful control over the inputs, parameters, or training data. This does not include the mere relaying of requests to models hosted by other third parties. 
+“AI Work Assistant” means an independent AI-powered product primarily designed for AI-assisted coding or office productivity (e.g., Qoder and QwenWork). It does not include: (a) a single-purpose AI tool (such as an AI translation tool); (b) an AI assistant primarily designed for a domain other than coding or office productivity (such as Taobao AI Shopping Assistant or AMap AI Chat); or (c) an AI assistant that is a feature of a product whose primary purpose is not AI-assisted coding or office productivity.
+
+THE SOFTWARE AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL QWEN, ITS AFFILIATES OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. THE USE OF THE SOFTWARE MUST COMPLY WITH APPLICABLE LAWS AND REGULATIONS, AND MUST NOT INFRINGE THE INTELLECTUAL PROPERTY RIGHTS OF ANY THIRD PARTY.
+
+For any questions regarding this license, please contact model-business@notice.qwencloud.com.

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+    sha256: 3cffb8fcc5f070d34bff3eddc67a721b26f284b0ec71f5da5aecda8b468543a7
+  - bytes: 12800098304
+    name: ple-fp8-00002-of-00004.bin
+    sha256: f2f63094e120bc207d6ad72117343b911a62b24ad659e99467eddf5f6ba23a36
+  - bytes: 12800098304
+    name: ple-fp8-00003-of-00004.bin
+    sha256: a4396a17923598c1905820102e0a996f7b8f9032b5edbb30d734bcdef932ce0d
+  - bytes: 12800098304
+    name: ple-fp8-00004-of-00004.bin
+    sha256: deeaaf2efd5f1983cfd2306a6da5135f4c2731a73d977e09de270ea097a65c16
+  independent_sha256_verification: passed_4_of_4
+  logical_parts: 128
+  method: copy official FP8 after complete Swift/Qwen BF16 PLE identity
+  parameters: 51200245760
+  payload_bytes: 51200245760
+  repository_path: PLE-FP8
+  source:
+    repo: Qwen/Qwen3.8-Flash-Next-FP8
+    revision: 236dfdf285828023ca3bcd3f37366c58a3469b13
+verification:
+  ple_plan_equivalence: passed_exact_layout
+  q5_recipe_equivalence: passed_zero_changed_tensors
+  remote_lfs: partially_verified_or_pending_upload
+  runtime: not_run_in_this_conversion_task
+  source_topology: passed
+license:
+  swift: Swift_Open_License_v1.0
+  base_model: Qwen_Community_License_1.0

MQ-Q5-SSD-PLE-BF16/verify-main-gguf.json

@@ -0,0 +1,81 @@
+{
+  "errors": [],
+  "files": [
+    {
+      "data_offset": 10983136,
+      "declared_end": 32066883712,
+      "file_bytes": 32066883712,
+      "metadata": {
+        "general.alignment": 32,
+        "general.architecture": "qwen4exp",
+        "general.license": "other",
+        "general.license.link": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/0bd4fe22431372cdad1979267d3ab45aa7e6150a/LICENSE",
+        "general.license.name": "swift-open-license-1.0",
+        "general.name": "Swift1.5-Qwen3.8-Flash-Next MQ-Q5-SSD-PLE-BF16",
+        "general.quantization": "MQ-Q5-SSD-PLE-BF16",
+        "split.count": 3,
+        "split.no": 0,
+        "split.tensors.count": 1628
+      },
+      "metadata_count": 99,
+      "path": "/mnt/p31/data/swift1.5-qwen3.8-flash-next/publish/MQ-Q5-SSD-PLE-BF16/Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00001-of-00003.gguf",
+      "template_only": false,
+      "tensor_count": 498,
+      "version": 3
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+    {
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+      "template_only": false,
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+  ],
+  "ok": true,
+  "schema_version": 1,
+  "shard_count": 3,
+  "source_map": "/mnt/p31/data/swift1.5-qwen3.8-flash-next/publish/reproduction/manifests/swift/ssd-ple-q5/main-gguf-source-map.json",
+  "tensor_count": 1628,
+  "type_bytes": {
+    "BF16": 1593061376,
+    "F32": 263372224,
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+    "Q5_K": 26759659520,
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+  "variant": "MQ-Q5-SSD-PLE-BF16"
+}

NOTICE

@@ -0,0 +1,34 @@
+Swift-Qwen3.8-Flash-Next
+Copyright 2026 UkisAI
+
+UkisAI's contribution (the "Swift Contribution") is licensed under the
+Swift Open License v1.0. See LICENSE.
+
+This model is a derivative work of Qwen3.8-Flash-Next
+  https://huggingface.co/Qwen/Qwen3.8-Flash-Next
+  Copyright (c) 2026 Qwen
+  Licensed under the Qwen Community License 1.0. See LICENSE-QWEN.
+
+Changes made by UkisAI:
+  - model-*.safetensors: model weights were modified by UkisAI through fine-tuning.
+  - tokenizer.json, tokenizer_config.json: re-serialized from UkisAI's training
+    base. Vocabulary and merges are unchanged; the file format, some metadata
+    fields and the pre-tokenizer pattern differ from the Base Model files.
+  - README.md: replaced. LICENSE (Swift Open License v1.0), NOTICE,
+    benchmarks/ and ukisai-banner.png added.
+  - The Base Model's license text was moved from LICENSE to LICENSE-QWEN.
+  - All other files (config.json, generation_config.json, chat_template.jinja,
+    model.safetensors.index.json, vocab.json, merges.txt,
+    preprocessor_config.json, video_preprocessor_config.json) are unmodified
+    from Qwen3.8-Flash-Next and remain under the Qwen Community License 1.0.
+
+Changes made by Baekpica:
+  - Main GGUF files: converted Swift1.5 BF16 weights using the published
+    Qwen MQ-Q5 SSD-PLE tensor recipe; no pruning or imatrix calibration.
+  - PLE-FP8/: official Qwen FP8 PLE copied after full byte-exact verification
+    that Swift BF16 PLE is unchanged from the Qwen BF16 source.
+  - reproduction/: conversion and verification tools adapted for Swift.
+  - README.md and artifact manifests: added conversion and provenance records.
+
+This is an independent conversion, not an official UkisAI or Qwen release.
+The ds4 quantizer sources are MIT licensed; see ds4/LICENSE.

PLE-FP8/README.md

@@ -0,0 +1,18 @@
+# Official Qwen FP8 PLE, reused for Swift1.5
+
+These four files and the 2-byte BF16 scale were copied from the local Qwen
+FP8 sidecar after complete Swift/Qwen BF16 PLE identity was proven. The
+weights, scale and manifest are unchanged from the Qwen release.
+
+Original source: `Qwen/Qwen3.8-Flash-Next-FP8@236dfdf285828023ca3bcd3f37366c58a3469b13`.
+Storage: `FP8_E4M3FN`, 128 logical parts, 160-byte rows, 4,096-byte alignment,
+shared BF16 multiplicative scale `0x3951`.
+
+Decode each E4M3FN code to BF16, multiply by the BF16 scale, and round to BF16.
+The runtime keeps compressed pages in a bounded cache.
+
+`verify-extraction.json` and `verify-independent.json` are the original Qwen
+extraction receipts. `hashes-independent.json` records the fresh copied-file
+verification for this package. Run `sha256sum -c SHA256SUMS` from this directory.
+
+See the [root card](../README.md) and [reproduction](../reproduction/README.md).

PLE-FP8/SHA256SUMS

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+a4396a17923598c1905820102e0a996f7b8f9032b5edbb30d734bcdef932ce0d  ple-fp8-00003-of-00004.bin
+deeaaf2efd5f1983cfd2306a6da5135f4c2731a73d977e09de270ea097a65c16  ple-fp8-00004-of-00004.bin
+c7c58bd6007672362da2106fdbfaf9f50629e4bdf8598169c598027394ef9791  ple-fp8-weight-scale.bf16.bin
+507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e  ple-manifest.json

PLE-FP8/hashes-independent.json

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+{
+  "algorithm": "sha256",
+  "elapsed_seconds": 103.63993384398054,
+  "file_count": 6,
+  "files": [
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+      "bytes": 12800098304,
+      "name": "ple-fp8-00001-of-00004.bin",
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+      "name": "ple-manifest.json",
+      "sha256": "507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e"
+    }
+  ],
+  "schema_version": 1,
+  "total_bytes": 51200476249
+}

PLE-FP8/ple-fp8-00001-of-00004.bin

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PLE-FP8/ple-fp8-00002-of-00004.bin

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PLE-FP8/ple-fp8-00003-of-00004.bin

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+size 12800098304

PLE-FP8/ple-fp8-00004-of-00004.bin

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PLE-FP8/ple-fp8-weight-scale.bf16.bin

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PLE-FP8/ple-manifest.json

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+{
+  "alignment_bytes": 4096,
+  "artifact_scope": "PLE sidecar only; main GGUF weights are not included",
+  "artifact_variant": "PLE-FP8",
+  "auxiliary_file_bytes": 2,
+  "byte_order": "little",
+  "checksum_status": "verified_source_fp8_and_physical_files",
+  "conversion": "bitwise extraction; no requantization, dequantization, or rescaling",
+  "embedding_row_dimension": 160,
+  "format_version": 2,
+  "hash_reference": {
+    "implementation": "transformers.models.qwen4_exp.modeling_qwen4_exp.Qwen4ExpTextNGramEmbedding",
+    "implementation_sha256": "77fec77d87f2a0eb23b95fa04276fb5779698a7c7f523cf5061e49c118bcc459",
+    "semantics": "SplitMix64-derived odd multipliers; multiply token ids; XOR in n-gram position order; modulo per-head prime vocabulary; add per-head offset; EOS-bounded history",
+    "transformers_version": "5.16.0"
+  },
+  "hash_topology_reference": {
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+    "source": {
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+      "revision": "f5d08274bafd880402bd16f5e3e6c514136ec06c"
+    }
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+  "layer_multipliers": [
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+    20109073645365,
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+    "scale_scope": "one scalar shared by all 128 logical parts",
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+  },
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+  "source": {
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+    "revision": "236dfdf285828023ca3bcd3f37366c58a3469b13"
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+      "source_name": "model.language_model.layers.1.ple.ple_embedding.ngram_heads_vocab_sizes",
+      "source_shard": "model-00037-of-00131.safetensors"
+    }
+  ],
+  "source_safetensors_dtype": "F8_E4M3",
+  "storage": "ssd_backed_bounded_page_cache",
+  "storage_dtype": "FP8_E4M3FN",
+  "total_file_bytes_including_alignment": 51200393216,
+  "total_parameter_count": 51200245760,
+  "total_payload_bytes": 51200245760,
+  "total_sidecar_bytes_including_scale": 51200393218,
+  "usable_vocabulary_rows": 320001446
+}

PLE-FP8/verify-extraction.json

@@ -0,0 +1,185 @@
+{
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+}

PLE-FP8/verify-independent.json

@@ -0,0 +1,18 @@
+{
+  "ok": true,
+  "source": {
+    "repo": "Qwen/Qwen3.8-Flash-Next-FP8",
+    "revision": "236dfdf285828023ca3bcd3f37366c58a3469b13"
+  },
+  "manifest_sha256": "507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e",
+  "logical_parts_fully_hashed": 128,
+  "physical_files_fully_hashed": 4,
+  "all_padding_zero": true,
+  "nan_codes_in_full_payload": 0,
+  "payload_bytes_scanned": 51200245760,
+  "scale_matches_source_bytes": true,
+  "sampled_rows": 2048,
+  "sampled_raw_bytes_match_safetensors": true,
+  "sampled_bf16_decoding_matches_source_and_independent_numpy": true,
+  "runtime_inference_tested": false
+}

README.md

@@ -11,83 +11,64 @@ tags:
   - qwen4exp
   - qwen3.8-flash-next
   - swift1.5
-  - dgx-spark
   - ds4
+  - dgx-spark
   - ssd-offload
 ---
 
 # Swift1.5-Qwen3.8-Flash-Next Mixed-Quant GGUF
 
-> **Preparation in progress.** This model card is published before the weight files. The BF16 source download and CPU conversion are being prepared; the GGUF weights and FP8 PLE sidecars have not been uploaded yet. Final file sizes, checksums and download commands will be added after verification.
->
-> **One smaller Q5 recipe with FP8 SSD-PLE is planned.** The main tensor precision map matches the published Qwen3.8 and Uncensored `MQ-Q5-SSD-PLE-BF16` recipe. The identical Swift/Qwen BF16 PLE table allows reuse of the existing official Qwen FP8 sidecar. No imatrix is used.
+> **One smaller Q5 compute backbone with an official FP8 SSD-PLE sidecar.**
+> The main precision map matches the published Qwen3.8 and Uncensored
+> `MQ-Q5-SSD-PLE-BF16` recipe. All 128 Swift BF16 PLE parts match Qwen,
+> allowing the existing official FP8 PLE files to be reused unchanged.
 
 ## Support my work
 
 Support model conversion, inference optimization, profiling and open validation:
 [Buy Me a Coffee](https://www.buymeacoffee.com/baekpica) · [GitHub Sponsors](https://github.com/sponsors/Baekpica).
 
-This is an independent Baekpica conversion of
-[ukisai/Swift1.5-Qwen3.8-Flash-Next](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next),
-pinned to revision [`0bd4fe22431372cdad1979267d3ab45aa7e6150a`](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/tree/0bd4fe22431372cdad1979267d3ab45aa7e6150a).
+Independent Baekpica conversion of
+[ukisai/Swift1.5-Qwen3.8-Flash-Next](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/tree/0bd4fe22431372cdad1979267d3ab45aa7e6150a).
+The main weights use the same smaller `MQ-Q5-SSD-PLE-BF16` tensor recipe as
+the published Qwen3.8 and Uncensored SSD-PLE models. Conversion ran on CPU
+from source BF16 weights, with no imatrix, pruning, expert dropping or layer dropping.
 
 The memory hierarchy follows the existing
 [Qwen3.8 mixed-quant SSD-PLE release](https://huggingface.co/Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF):
-the 51.2B-parameter predictive latent embedding (PLE) table resides on SSD,
-with a bounded runtime page cache, while the 128.8B-parameter compute backbone
-uses a mixed-precision GGUF. This package is intended for the dedicated
-[`ds4-dfm-rs`](https://github.com/Baekpica/ds4-dfm-rs) `qwen4exp` SSD-PLE loader.
-Generic GGUF runtime compatibility is not established.
-
-## Planned variant and storage
-
-| Component | Format | Size | Current status |
-|---|---|---:|---|
-| Main, `MQ-Q5-SSD-PLE-BF16` | Mixed Q4_K / Q5_K / Q5_0 / Q8_0 / BF16 / F32 / I64 | 77.5456 GiB tensor payload, planned | Source downloading; conversion pending |
-| PLE, `PLE-FP8/` | FP8 E4M3FN, 4 files / 128 logical parts | 51,200,393,216 bytes / 47.6841 GiB, verified locally | Copied and checked; upload pending |
-| Required PLE scale | One BF16 scalar, `0x3951` | 2 bytes | Copied and checked; upload pending |
-
-The main payload size is derived from the exact tensor recipe; it is not a
-completed GGUF file measurement. Final GGUF metadata and alignment add a small
-amount of storage. PLE cache, KV state, checkpoints and runtime workspaces are
-separate memory costs. The legacy `MQ-Q5-SSD-PLE-BF16` directory name describes
-the main packaging contract; `DS4_QWEN_PLE_DIR` selects the FP8 PLE manifest.
+the 51.2B-parameter PLE table resides on SSD with a bounded runtime page cache,
+while the 128.8B-parameter compute backbone uses a mixed-precision GGUF.
+
+## Variant and storage
+
+| Artifact | Storage |
+|---|---:|
+| Main GGUF, 3 shards / 1,628 tensors | 83,274,984,576 bytes / 77.5559 GiB |
+| Main tensor payload | 83,263,928,920 bytes / 77.5456 GiB |
+| FP8 PLE, 4 files / 128 logical parts | 51,200,393,216 bytes / 47.6841 GiB |
+| FP8 scale | 2 bytes, BF16 `0x3951` |
+| Total weight files, including scale | 134,475,377,794 bytes / 125.2400 GiB |
+
+GGUF metadata/alignment, PLE cache, KV state and runtime workspaces are separate costs.
 
 ## Quantization targets
 
-| Model region | Type | Residency policy |
-|---|---|---|
-| Routed expert gate/up, interior layers 2–45 | Q4_K | Accelerator-resident |
-| Routed expert gate/up, edge layers 0, 1, 46, 47 | Q5_K | Accelerator-resident |
-| Routed expert down, main 512 columns | Q5_K | Accelerator-resident |
-| Routed expert down, 128-column tail | Q5_0 | Accelerator-resident |
-| MTP routed experts and most always-active matrices | Primarily Q8_0 | Accelerator-resident |
-| Non-quantizable tensors, norms and controls | BF16 / F32 / I64 | Accelerator-resident |
-| PLE n-gram table | FP8 E4M3FN | SSD-backed, bounded cache |
-
-Conversion will run directly from the pinned Swift BF16 checkpoint on CPU.
-There is no requantization of an existing GGUF, imatrix calibration, pruning,
-expert dropping, expert merging or layer dropping. The target tensor map
-preserves embedded MTP, vision tensors, integer PLE controls and the source
-chat template. The full tensor map and reproduction scripts will accompany
-the weight release.
-
-## FP8 PLE provenance
-
-Local preparation proved equality of all 128 Swift BF16 PLE parts with the
-Qwen BF16 reference. Matching pinned whole-shard SHA-256 values prove 124
-parts; direct tensor-range SHA-256 proves the four parts in the two modified
-mixed shards. PLE controls and layout also match.
-
-The existing Qwen FP8 sidecar was then copied unchanged and all six checksum
-entries passed locally. Its source is
-[`Qwen/Qwen3.8-Flash-Next-FP8@236dfdf285828023ca3bcd3f37366c58a3469b13`](https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8/tree/236dfdf285828023ca3bcd3f37366c58a3469b13).
-It preserves 160-byte rows, 4,096-byte alignment and the original shared BF16
-multiplicative scale. This is reuse of the official Qwen FP8 PLE, rather than
-a separate Swift FP8 checkpoint. The identity and hash reports will be included
-with the final package.
-
-## Planned artifact layout
+| Region | Type |
+|---|---|
+| Routed gate/up, interior layers 2–45 | Q4_K |
+| Routed gate/up, edge layers 0, 1, 46, 47 | Q5_K |
+| Routed down, main 512 columns | Q5_K |
+| Routed down, 128-column tail | Q5_0 |
+| MTP and most always-active matrices | Primarily Q8_0 |
+| Non-quantizable matrices, norms and controls | BF16 / F32 / I64 |
+| PLE n-gram table on SSD | FP8 E4M3FN |
+
+The exact tensor map is in
+[`main-gguf-source-map.json`](reproduction/manifests/swift/ssd-ple-q5/main-gguf-source-map.json).
+The main GGUF retains embedded MTP, vision tensors, integer PLE controls and
+the source chat template. The standalone tokenizer and processor assets are also included.
+
+## Published artifact layout
 
 ```text
 MQ-Q5-SSD-PLE-BF16/       Three main GGUF shards, recipe and checksums
@@ -95,29 +76,55 @@ PLE-FP8/                 Four FP8 embedding files, scale and manifest
 reproduction/            Converter, tensor maps and verification reports
 ```
 
-Tokenizer, processor, source chat-template assets, licenses and notices will
-also be included. Download and serving instructions will be added once the
-weight files are available.
+## PLE provenance
+
+All 128 Swift BF16 PLE parts match the Qwen BF16 reference. Complete pinned
+source-shard SHA-256 proves 124 parts; direct tensor-range SHA-256 proves the
+four parts in the two modified mixed shards. The
+[`identity report`](reproduction/manifests/swift/verify-ple-identity.json) records both methods.
+
+The official [Qwen FP8 PLE](https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8/tree/236dfdf285828023ca3bcd3f37366c58a3469b13)
+was copied from the local Qwen model after that complete identity proof.
+It is the same FP8 sidecar used by the Baekpica Qwen3.8 and Uncensored releases.
+It is not a separate Swift FP8 checkpoint or a new BF16-to-FP8 quantization.
+
+## Download and use with ds4-dfm-rs
+
+This is the dedicated `qwen4exp` SSD-PLE format. Use
+[`Baekpica/ds4-dfm-rs`](https://github.com/Baekpica/ds4-dfm-rs), with its FP8 PLE loader;
+generic GGUF runtime compatibility is not established.
+
+```bash
+swift_repo=Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF
+swift_root=./Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF
+hf download "$swift_repo" --local-dir "$swift_root"
+(cd "$swift_root" && sha256sum -c SHA256SUMS)
+export DS4_QWEN_PLE_DIR="$(realpath "$swift_root/PLE-FP8")"
+./ds4 --cuda -m "$swift_root/MQ-Q5-SSD-PLE-BF16/Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00001-of-00003.gguf" \
+  -c 8192 -n 128 -p "Explain binary search."
+```
+
+Set `DS4_QWEN_PLE_DIR` explicitly: this package contains FP8 PLE, while the
+main packaging name and input precision remain `MQ-Q5-SSD-PLE-BF16`.
 
-## Validation status
+## Verification
 
-Completed local preparation: exact Q5 tensor-map equivalence, PLE layout and
-controls, complete BF16 PLE identity, and checksums of the copied FP8 sidecar.
-Full source checksums, source topology, CPU conversion, final GGUF structure,
-independent final hashes and public weight verification remain pending.
+Source-file checksums, all 1,658 source tensors, the 1,628-tensor Q5 recipe,
+PLE controls/layout, complete BF16 PLE identity, main GGUF structure and
+independent main/FP8 hashes passed. See [`artifact-manifest.json`](artifact-manifest.json),
+[`SHA256SUMS`](SHA256SUMS) and [`reproduction/`](reproduction/README.md).
 
-Swift GPU inference, serving, throughput and task-quality evaluation have not
-been run for this conversion. The performance charts and qualification results
-on the Qwen and Uncensored cards describe those models; they are not Swift
-measurements.
+Swift GPU inference, serving, throughput and task-quality evaluation were not
+run in this conversion task. Existing Qwen or Uncensored performance and quality
+results do not qualify these Swift main weights.
 
 ## License and attribution
 
-Swift Contribution: [Swift Open License v1.0](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/0bd4fe22431372cdad1979267d3ab45aa7e6150a/LICENSE), Copyright 2026 UkisAI.
-Base model: [Qwen Community License 1.0](https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/0bd4fe22431372cdad1979267d3ab45aa7e6150a/LICENSE-QWEN), Copyright 2026 Qwen.
-The original licenses and notices will accompany the weights. The release will
-also include a notice describing the conversion and reused FP8 sidecar.
+Swift Contribution: [Swift Open License v1.0](LICENSE), Copyright 2026 UkisAI.
+Base model: [Qwen Community License 1.0](LICENSE-QWEN), Copyright 2026 Qwen.
+Both licenses and the original notices are included. [`NOTICE`](NOTICE) records
+the conversion changes. Quantizer code is MIT licensed; see [`ds4/LICENSE`](ds4/LICENSE).
 
 Thanks to UkisAI for Swift1.5, Qwen for the base model and FP8 PLE, GGML/llama.cpp
 for the quantization foundations, and the DwarfStar/ds4 contributors.
-This is not an official UkisAI or Qwen release.
+This conversion is not an official UkisAI or Qwen release.

SHA256SUMS

@@ -0,0 +1,9 @@
+8e4c9e2fb1898a55a02c346b81e38c9612d342bf8d5efaca76b9a40273de31f2  MQ-Q5-SSD-PLE-BF16/Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00001-of-00003.gguf
+88d099ef89af5d2a885c6c7d9e7305a8183ee8c6ac53eed574a36cde7d119f5f  MQ-Q5-SSD-PLE-BF16/Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00002-of-00003.gguf
+cca1421d8b8da7634f304dc4519cb5294be7aff187a6d5842410954bed6bdb26  MQ-Q5-SSD-PLE-BF16/Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00003-of-00003.gguf
+3cffb8fcc5f070d34bff3eddc67a721b26f284b0ec71f5da5aecda8b468543a7  PLE-FP8/ple-fp8-00001-of-00004.bin
+f2f63094e120bc207d6ad72117343b911a62b24ad659e99467eddf5f6ba23a36  PLE-FP8/ple-fp8-00002-of-00004.bin
+a4396a17923598c1905820102e0a996f7b8f9032b5edbb30d734bcdef932ce0d  PLE-FP8/ple-fp8-00003-of-00004.bin
+deeaaf2efd5f1983cfd2306a6da5135f4c2731a73d977e09de270ea097a65c16  PLE-FP8/ple-fp8-00004-of-00004.bin
+c7c58bd6007672362da2106fdbfaf9f50629e4bdf8598169c598027394ef9791  PLE-FP8/ple-fp8-weight-scale.bf16.bin
+507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e  PLE-FP8/ple-manifest.json

artifact-manifest.json

@@ -0,0 +1,114 @@
+{
+  "main_gguf": {
+    "file_bytes": 83274984576,
+    "file_count": 3,
+    "files": [
+      {
+        "bytes": 32066883712,
+        "name": "Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00001-of-00003.gguf",
+        "sha256": "8e4c9e2fb1898a55a02c346b81e38c9612d342bf8d5efaca76b9a40273de31f2"
+      },
+      {
+        "bytes": 31811595296,
+        "name": "Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00002-of-00003.gguf",
+        "sha256": "88d099ef89af5d2a885c6c7d9e7305a8183ee8c6ac53eed574a36cde7d119f5f"
+      },
+      {
+        "bytes": 19396505568,
+        "name": "Swift1.5-Qwen3.8-Flash-Next-MQ-Q5-SSD-PLE-BF16-00003-of-00003.gguf",
+        "sha256": "cca1421d8b8da7634f304dc4519cb5294be7aff187a6d5842410954bed6bdb26"
+      }
+    ],
+    "independent_sha256_verification": "passed_3_of_3",
+    "logical_parameters": 128799735699,
+    "payload_bytes": 83263928920,
+    "structure_verification": "passed_zero_errors",
+    "target_type_counts": {
+      "BF16": 363,
+      "F32": 584,
+      "I64": 3,
+      "Q4_K": 88,
+      "Q5_0": 48,
+      "Q5_K": 56,
+      "Q8_0": 486
+    },
+    "tensor_count": 1628
+  },
+  "ple_sidecar": {
+    "auxiliary_files": [
+      {
+        "bytes": 2,
+        "name": "ple-fp8-weight-scale.bf16.bin",
+        "sha256": "c7c58bd6007672362da2106fdbfaf9f50629e4bdf8598169c598027394ef9791"
+      },
+      {
+        "bytes": 83031,
+        "name": "ple-manifest.json",
+        "sha256": "507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e"
+      }
+    ],
+    "bf16_source_identity": "passed_128_of_128",
+    "dtype": "FP8_E4M3FN",
+    "file_bytes_including_alignment": 51200393216,
+    "file_count": 4,
+    "files": [
+      {
+        "bytes": 12800098304,
+        "name": "ple-fp8-00001-of-00004.bin",
+        "sha256": "3cffb8fcc5f070d34bff3eddc67a721b26f284b0ec71f5da5aecda8b468543a7"
+      },
+      {
+        "bytes": 12800098304,
+        "name": "ple-fp8-00002-of-00004.bin",
+        "sha256": "f2f63094e120bc207d6ad72117343b911a62b24ad659e99467eddf5f6ba23a36"
+      },
+      {
+        "bytes": 12800098304,
+        "name": "ple-fp8-00003-of-00004.bin",
+        "sha256": "a4396a17923598c1905820102e0a996f7b8f9032b5edbb30d734bcdef932ce0d"
+      },
+      {
+        "bytes": 12800098304,
+        "name": "ple-fp8-00004-of-00004.bin",
+        "sha256": "deeaaf2efd5f1983cfd2306a6da5135f4c2731a73d977e09de270ea097a65c16"
+      }
+    ],
+    "independent_sha256_verification": "passed_4_of_4",
+    "logical_parts": 128,
+    "method": "copy official FP8 after complete Swift/Qwen BF16 PLE identity",
+    "parameters": 51200245760,
+    "payload_bytes": 51200245760,
+    "repository_path": "PLE-FP8",
+    "source": {
+      "repo": "Qwen/Qwen3.8-Flash-Next-FP8",
+      "revision": "236dfdf285828023ca3bcd3f37366c58a3469b13"
+    }
+  },
+  "recipe_reference": {
+    "q5_source_map_sha256": "bedb8b03cd6a06072c1e0b31eda42fc8be4642d22a5889ebd5da10d902fd3de9",
+    "repo": "Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF",
+    "revision": "95e1cdf0c37eece99886a078bb7d68e3ababdb2f"
+  },
+  "remote_verification": {
+    "main_gguf": {
+      "status": "pending_upload"
+    },
+    "ple_sidecar": {
+      "status": "pending_upload"
+    }
+  },
+  "schema_version": 1,
+  "source": {
+    "repo": "ukisai/Swift1.5-Qwen3.8-Flash-Next",
+    "revision": "0bd4fe22431372cdad1979267d3ab45aa7e6150a"
+  },
+  "total_weight_file_bytes": 134475377794,
+  "variant": "MQ-Q5-SSD-PLE-BF16",
+  "verification": {
+    "ple_plan_equivalence": "passed_exact_layout",
+    "q5_recipe_equivalence": "passed_zero_changed_tensors",
+    "remote_lfs": "partially_verified_or_pending_upload",
+    "runtime": "not_run_in_this_conversion_task",
+    "source_topology": "passed"
+  }
+}

chat_template.jinja

@@ -0,0 +1,170 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- set reasoning_instructions = '' %}
+{%- if enable_thinking is undefined or enable_thinking is true %}
+    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}
+    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}
+        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}
+    {%- endif %}
+    {%- if resolved_reasoning_effort == 'xhigh' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}
+    {%- elif resolved_reasoning_effort == 'low' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}
+    {%- endif %}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {%- if reasoning_instructions %}
+        {{- reasoning_instructions + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '<|im_start|>system\n' + (reasoning_instructions + '\n\n' if reasoning_instructions else '')  + content + '<|im_end|>\n' }}
+        {%- elif reasoning_instructions %}
+            {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+        {%- endif %}
+    {%- elif reasoning_instructions %}
+        {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined and tool_call.arguments != '' %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,154 @@
+{
+    "architectures": [
+        "Qwen4ExpForConditionalGeneration"
+    ],
+    "image_token_id": 248056,
+    "language_model_only": false,
+    "model_type": "qwen4_exp",
+    "text_config": {
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "bos_token_id": 248044,
+        "dtype": "bfloat16",
+        "eos_token_id": 248044,
+        "full_attention_interval": 4,
+        "hc_count": 4,
+        "hc_lowrank": 320,
+        "head_dim": 256,
+        "heads_per_ngram": 8,
+        "hidden_act": "silu",
+        "hidden_size": 2560,
+        "indexer_budget": 2048,
+        "indexer_compress_ratio": 4,
+        "indexer_head_dim": 128,
+        "indexer_kv_heads": 1,
+        "indexer_n_heads": 4,
+        "initializer_range": 0.02,
+        "layer_types": [
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention",
+            "linear_attention",
+            "linear_attention",
+            "linear_attention",
+            "full_attention"
+        ],
+        "linear_conv_kernel_dim": 4,
+        "linear_key_head_dim": 128,
+        "linear_num_key_heads": 16,
+        "linear_num_value_heads": 48,
+        "linear_value_head_dim": 128,
+        "make_ngram_vocab_size_divisible_by": 128,
+        "mamba_ssm_dtype": "float32",
+        "max_position_embeddings": 262144,
+        "model_type": "qwen4_exp_text",
+        "moe_intermediate_size": 640,
+        "mtp": {
+            "hybrid": true,
+            "layer_types": [
+                "full_attention"
+            ],
+            "mtp_use_hidden_state_from_layer": null,
+            "num_hidden_layers": 1,
+            "rope_theta": 10000000
+        },
+        "mtp_num_hidden_layers": 1,
+        "mtp_use_dedicated_embeddings": false,
+        "ngram_size": 3,
+        "ngram_vocab_size_base": 20000000,
+        "num_attention_heads": 24,
+        "num_experts": 512,
+        "num_experts_per_tok": 10,
+        "num_hidden_layers": 48,
+        "num_key_value_heads": 2,
+        "output_gate_type": "sigmoid",
+        "output_router_logits": false,
+        "pad_token_id": null,
+        "partial_rotary_factor": 0.25,
+        "ple_conv_kernel_size": 4,
+        "ple_embed_dim": 2560,
+        "ple_layer_ids": [
+            2
+        ],
+        "rms_norm_eps": 1e-06,
+        "rope_parameters": {
+            "mrope_interleaved": true,
+            "mrope_section": [
+                11,
+                11,
+                10
+            ],
+            "partial_rotary_factor": 0.25,
+            "rope_theta": 10000000,
+            "rope_type": "default"
+        },
+        "router_aux_loss_coef": 0.001,
+        "shared_expert_intermediate_size": 640,
+        "split_ngram_parts": 128,
+        "tie_word_embeddings": false,
+        "use_cache": true,
+        "vocab_size": 248320
+    },
+    "tie_word_embeddings": false,
+    "transformers_version": "5.8.0.dev0",
+    "video_token_id": 248057,
+    "vision_config": {
+        "deepstack_visual_indexes": [],
+        "depth": 27,
+        "hidden_act": "gelu_pytorch_tanh",
+        "hidden_size": 1152,
+        "in_channels": 3,
+        "initializer_range": 0.02,
+        "intermediate_size": 4304,
+        "model_type": "qwen4_exp",
+        "num_heads": 16,
+        "num_position_embeddings": 2304,
+        "out_hidden_size": 2560,
+        "patch_size": 16,
+        "spatial_merge_size": 2,
+        "temporal_patch_size": 2
+    },
+    "vision_end_token_id": 248054,
+    "vision_start_token_id": 248053
+}

ds4/LICENSE

@@ -0,0 +1,23 @@
+MIT License
+
+Copyright (c) 2026 The ds4.c authors
+Copyright (c) 2026 Entrpi <entrpi@proton.me> (batched-serving fork modifications)
+Copyright (c) 2023-2026 The ggml authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ds4/gguf-tools/quants.c

@@ -0,0 +1,1365 @@
+/*
+ * DS4 quantization facade.
+ *
+ * These are the small GGUF quantization pieces needed by our DeepSeek V4
+ * Flash recipes: float conversion, q8_0, q2_K, q4_K, q5_K, q6_K, and
+ * iq2_xxs.  The code is
+ * local C and deliberately narrow; other GGUF type IDs are named for metadata
+ * compatibility, but cannot be emitted by this tool.
+ *
+ * The quantized block layouts and search procedures are derived from the
+ * MIT-licensed GGML/llama.cpp quantizers.  Keep changes conservative: byte
+ * layout compatibility is more important here than generality.
+ */
+
+#include "quants.h"
+
+#include <assert.h>
+#include <float.h>
+#include <math.h>
+#include <pthread.h>
+#include <stdint.h>
+#include <stdlib.h>
+#include <string.h>
+
+static pthread_mutex_t ds4q_init_mutex = PTHREAD_MUTEX_INITIALIZER;
+
+typedef struct {
+    const char *name;
+    int64_t block_size;
+    size_t type_size;
+    bool can_quantize;
+    bool requires_imatrix;
+} ds4q_traits;
+
+#define QK_K 256
+#define DS4Q_GROUP_MAX_EPS 1e-15f
+#define DS4Q_MIN(a, b) ((a) < (b) ? (a) : (b))
+#define DS4Q_MAX(a, b) ((a) > (b) ? (a) : (b))
+
+static const ds4q_traits ds4q_type_traits[DS4Q_TYPE_COUNT] = {
+    [DS4Q_TYPE_F32]     = { "f32",      1,   4, false, false },
+    [DS4Q_TYPE_F16]     = { "f16",      1,   2, false, false },
+    [DS4Q_TYPE_Q4_0]    = { "q4_0",    32,  18, false, false },
+    [DS4Q_TYPE_Q4_1]    = { "q4_1",    32,  20, false, false },
+    [DS4Q_TYPE_Q5_0]    = { "q5_0",    32,  22, false, false },
+    [DS4Q_TYPE_Q5_1]    = { "q5_1",    32,  24, false, false },
+    [DS4Q_TYPE_Q8_0]    = { "q8_0",    32,  34, true,  false },
+    [DS4Q_TYPE_Q8_1]    = { "q8_1",    32,  36, false, false },
+    [DS4Q_TYPE_Q2_K]    = { "q2_K",  QK_K,  84, true,  false },
+    [DS4Q_TYPE_Q3_K]    = { "q3_K",  QK_K, 110, false, false },
+    [DS4Q_TYPE_Q4_K]    = { "q4_K",  QK_K, 144, true,  false },
+    [DS4Q_TYPE_Q5_K]    = { "q5_K",  QK_K, 176, true,  false },
+    [DS4Q_TYPE_Q6_K]    = { "q6_K",  QK_K, 210, true,  false },
+    [DS4Q_TYPE_Q8_K]    = { "q8_K",  QK_K, 292, false, false },
+    [DS4Q_TYPE_IQ2_XXS] = { "iq2_xxs", QK_K,  66, true,  true  },
+    [DS4Q_TYPE_IQ2_XS]  = { "iq2_xs",  QK_K,  74, false, true  },
+    [DS4Q_TYPE_IQ3_XXS] = { "iq3_xxs", QK_K,  98, false, false },
+    [DS4Q_TYPE_IQ1_S]   = { "iq1_s",   QK_K,  50, false, true  },
+    [DS4Q_TYPE_IQ4_NL]  = { "iq4_nl",     32,  18, false, false },
+    [DS4Q_TYPE_IQ3_S]   = { "iq3_s",   QK_K, 110, false, false },
+    [DS4Q_TYPE_IQ2_S]   = { "iq2_s",   QK_K,  82, false, false },
+    [DS4Q_TYPE_IQ4_XS]  = { "iq4_xs",  QK_K, 136, false, false },
+    [DS4Q_TYPE_I8]      = { "i8",          1,   1, false, false },
+    [DS4Q_TYPE_I16]     = { "i16",         1,   2, false, false },
+    [DS4Q_TYPE_I32]     = { "i32",         1,   4, false, false },
+    [DS4Q_TYPE_I64]     = { "i64",         1,   8, false, false },
+    [DS4Q_TYPE_F64]     = { "f64",         1,   8, false, false },
+    [DS4Q_TYPE_IQ1_M]   = { "iq1_m",   QK_K,  56, false, false },
+    [DS4Q_TYPE_BF16]    = { "bf16",        1,   2, false, false },
+    [DS4Q_TYPE_TQ1_0]   = { "tq1_0",   QK_K,  54, false, false },
+    [DS4Q_TYPE_TQ2_0]   = { "tq2_0",   QK_K,  66, false, false },
+    [DS4Q_TYPE_MXFP4]   = { "mxfp4",      32,  17, false, false },
+    [DS4Q_TYPE_NVFP4]   = { "nvfp4",      64,  36, false, false },
+    [DS4Q_TYPE_Q1_0]    = { "q1_0",      128,  18, false, false },
+};
+
+static float ds4q_f32_from_bits(uint32_t bits) {
+    union {
+        uint32_t u;
+        float f;
+    } v = { .u = bits };
+    return v.f;
+}
+
+static uint32_t ds4q_f32_to_bits(float f) {
+    union {
+        float f;
+        uint32_t u;
+    } v = { .f = f };
+    return v.u;
+}
+
+static uint16_t ds4q_f32_to_f16(float f) {
+    const float scale_to_inf = 0x1.0p+112f;
+    const float scale_to_zero = 0x1.0p-110f;
+    float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+
+    const uint32_t w = ds4q_f32_to_bits(f);
+    const uint32_t shl1_w = w + w;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+    if (bias < UINT32_C(0x71000000)) bias = UINT32_C(0x71000000);
+
+    base = ds4q_f32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+    const uint32_t out = ds4q_f32_to_bits(base);
+    const uint32_t exp_bits = (out >> 13) & UINT32_C(0x00007C00);
+    const uint32_t mantissa_bits = out & UINT32_C(0x00000FFF);
+    const uint32_t nonsign = exp_bits + mantissa_bits;
+    return (uint16_t)((sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign));
+}
+
+static int ds4q_nearest_int(float fval) {
+    assert(fabsf(fval) <= 4194303.f);
+    float val = fval + 12582912.f;
+    int i;
+    memcpy(&i, &val, sizeof(i));
+    return (i & 0x007fffff) - 0x00400000;
+}
+
+static float ds4q_make_qkx2_quants(int n, int nmax, const float *x, const float *weights,
+                                   uint8_t *L, float *the_min, uint8_t *Laux,
+                                   float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights[0];
+    float sum_x = sum_w * x[0];
+    for (int i = 1; i < n; i++) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights[i];
+        sum_w += w;
+        sum_x += w * x[i];
+    }
+    if (min > 0) min = 0;
+    if (max == min) {
+        memset(L, 0, (size_t)n);
+        *the_min = -min;
+        return 0.0f;
+    }
+    float iscale = nmax / (max - min);
+    float scale = 1 / iscale;
+    float best_error = 0;
+    for (int i = 0; i < n; i++) {
+        int l = ds4q_nearest_int(iscale * (x[i] - min));
+        L[i] = DS4Q_MAX(0, DS4Q_MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff * diff;
+        best_error += weights[i] * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; is++) {
+        iscale = (rmin + rdelta * is + nmax) / (max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; i++) {
+            int l = ds4q_nearest_int(iscale * (x[i] - min));
+            l = DS4Q_MAX(0, DS4Q_MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights[i];
+            sum_l += w * l;
+            sum_l2 += w * l * l;
+            sum_xl += w * l * x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l) / D;
+            float this_min = (sum_l2 * sum_x - sum_l * sum_xl) / D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float cur_error = 0;
+            for (int i = 0; i < n; i++) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff * diff;
+                cur_error += weights[i] * diff;
+            }
+            if (cur_error < best_error) {
+                memcpy(L, Laux, (size_t)n);
+                best_error = cur_error;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
+static float ds4q_make_qkx3_quants(int n, int nmax, const float *x, const float *weights,
+                                   uint8_t *L, float *the_min, uint8_t *Laux,
+                                   float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights ? weights[0] : x[0] * x[0];
+    float sum_x = sum_w * x[0];
+    for (int i = 1; i < n; i++) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights ? weights[i] : x[i] * x[i];
+        sum_w += w;
+        sum_x += w * x[i];
+    }
+    if (min > 0) min = 0;
+    if (max <= min) {
+        memset(L, 0, (size_t)n);
+        *the_min = -min;
+        return 0.0f;
+    }
+    float iscale = nmax / (max - min);
+    float scale = 1 / iscale;
+    float best_mad = 0;
+    for (int i = 0; i < n; i++) {
+        int l = ds4q_nearest_int(iscale * (x[i] - min));
+        L[i] = DS4Q_MAX(0, DS4Q_MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff * diff;
+        float w = weights ? weights[i] : x[i] * x[i];
+        best_mad += w * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; is++) {
+        iscale = (rmin + rdelta * is + nmax) / (max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; i++) {
+            int l = ds4q_nearest_int(iscale * (x[i] - min));
+            l = DS4Q_MAX(0, DS4Q_MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights ? weights[i] : x[i] * x[i];
+            sum_l += w * l;
+            sum_l2 += w * l * l;
+            sum_xl += w * l * x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l) / D;
+            float this_min = (sum_l2 * sum_x - sum_l * sum_xl) / D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float mad = 0;
+            for (int i = 0; i < n; i++) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff * diff;
+                float w = weights ? weights[i] : x[i] * x[i];
+                mad += w * diff;
+            }
+            if (mad < best_mad) {
+                memcpy(L, Laux, (size_t)n);
+                best_mad = mad;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
+static float ds4q_make_qp_quants(int n, int nmax, const float *x, uint8_t *L, const float *quant_weights) {
+    float max = 0;
+    for (int i = 0; i < n; i++) max = DS4Q_MAX(max, x[i]);
+    if (max < DS4Q_GROUP_MAX_EPS) {
+        memset(L, 0, (size_t)n);
+        return 0.0f;
+    }
+    float iscale = nmax / max;
+    for (int i = 0; i < n; i++) L[i] = ds4q_nearest_int(iscale * x[i]);
+    float scale = 1 / iscale;
+    float best_mse = 0;
+    for (int i = 0; i < n; i++) {
+        float diff = x[i] - scale * L[i];
+        best_mse += quant_weights[i] * diff * diff;
+    }
+    for (int is = -4; is <= 4; is++) {
+        if (is == 0) continue;
+        float iscale_is = (0.1f * is + nmax) / max;
+        float scale_is = 1 / iscale_is;
+        float mse = 0;
+        for (int i = 0; i < n; i++) {
+            int l = ds4q_nearest_int(iscale_is * x[i]);
+            l = DS4Q_MIN(nmax, l);
+            float diff = x[i] - scale_is * l;
+            mse += quant_weights[i] * diff * diff;
+        }
+        if (mse < best_mse) {
+            best_mse = mse;
+            iscale = iscale_is;
+        }
+    }
+    float sumlx = 0, suml2 = 0;
+    for (int i = 0; i < n; i++) {
+        int l = ds4q_nearest_int(iscale * x[i]);
+        l = DS4Q_MIN(nmax, l);
+        L[i] = l;
+        float w = quant_weights[i];
+        sumlx += w * x[i] * l;
+        suml2 += w * l * l;
+    }
+    for (int itry = 0; itry < 5; itry++) {
+        int n_changed = 0;
+        for (int i = 0; i < n; i++) {
+            float w = quant_weights[i];
+            float slx = sumlx - w * x[i] * L[i];
+            float sl2 = suml2 - w * L[i] * L[i];
+            if (slx > 0 && sl2 > 0) {
+                int new_l = ds4q_nearest_int(x[i] * sl2 / slx);
+                new_l = DS4Q_MIN(nmax, new_l);
+                if (new_l != L[i]) {
+                    slx += w * x[i] * new_l;
+                    sl2 += w * new_l * new_l;
+                    if (slx * slx * suml2 > sumlx * sumlx * sl2) {
+                        L[i] = new_l;
+                        sumlx = slx;
+                        suml2 = sl2;
+                        n_changed++;
+                    }
+                }
+            }
+        }
+        if (!n_changed) break;
+    }
+    return suml2 > 0.0f ? sumlx / suml2 : 0.0f;
+}
+
+static void ds4q_get_scale_min_k4(int j, const uint8_t *q, uint8_t *d, uint8_t *m) {
+    if (j < 4) {
+        *d = q[j] & 63;
+        *m = q[j + 4] & 63;
+    } else {
+        *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
+        *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
+    }
+}
+
+static float ds4q_make_qx_quants(int n, int nmax, const float *x, int8_t *L,
+                                 int rmse_type, const float *quant_weights) {
+    float max = 0.0f;
+    float amax = 0.0f;
+    for (int i = 0; i < n; i++) {
+        const float ax = fabsf(x[i]);
+        if (ax > amax) {
+            amax = ax;
+            max = x[i];
+        }
+    }
+    if (amax < DS4Q_GROUP_MAX_EPS) {
+        memset(L, 0, (size_t)n);
+        return 0.0f;
+    }
+
+    float iscale = -nmax / max;
+    if (rmse_type == 0) {
+        for (int i = 0; i < n; i++) {
+            int l = ds4q_nearest_int(iscale * x[i]);
+            L[i] = nmax + DS4Q_MAX(-nmax, DS4Q_MIN(nmax - 1, l));
+        }
+        return 1.0f / iscale;
+    }
+
+    bool return_early = false;
+    if (rmse_type < 0) {
+        rmse_type = -rmse_type;
+        return_early = true;
+    }
+    float sumlx = 0.0f;
+    float suml2 = 0.0f;
+    for (int i = 0; i < n; i++) {
+        int l = ds4q_nearest_int(iscale * x[i]);
+        l = DS4Q_MAX(-nmax, DS4Q_MIN(nmax - 1, l));
+        L[i] = l + nmax;
+        const float w = quant_weights ? quant_weights[i] :
+            rmse_type == 1 ? x[i] * x[i] :
+            rmse_type == 2 ? 1.0f :
+            rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i]));
+        sumlx += w * x[i] * l;
+        suml2 += w * l * l;
+    }
+    float scale = suml2 ? sumlx / suml2 : 0.0f;
+    if (return_early) {
+        return suml2 > 0 ? 0.5f * (scale + 1.0f / iscale) : 1.0f / iscale;
+    }
+
+    float best = scale * sumlx;
+    for (int is = -9; is <= 9; is++) {
+        if (is == 0) continue;
+        iscale = -(nmax + 0.1f * is) / max;
+        sumlx = 0.0f;
+        suml2 = 0.0f;
+        for (int i = 0; i < n; i++) {
+            int l = ds4q_nearest_int(iscale * x[i]);
+            l = DS4Q_MAX(-nmax, DS4Q_MIN(nmax - 1, l));
+            const float w = quant_weights ? quant_weights[i] :
+                rmse_type == 1 ? x[i] * x[i] :
+                rmse_type == 2 ? 1.0f :
+                rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i]));
+            sumlx += w * x[i] * l;
+            suml2 += w * l * l;
+        }
+        if (suml2 > 0 && sumlx * sumlx > best * suml2) {
+            for (int i = 0; i < n; i++) {
+                int l = ds4q_nearest_int(iscale * x[i]);
+                L[i] = nmax + DS4Q_MAX(-nmax, DS4Q_MIN(nmax - 1, l));
+            }
+            scale = sumlx / suml2;
+            best = scale * sumlx;
+        }
+    }
+    return scale;
+}
+
+static size_t ds4q_quantize_q8_0(const float *src, void *dst, int64_t start,
+                                 int64_t nrows, int64_t ncols) {
+    const int64_t qk = 32;
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_Q8_0, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t nblocks = nrows * (ncols / qk);
+
+    for (int64_t b = 0; b < nblocks; b++) {
+        const float *x = src + start + (size_t)b * qk;
+        float amax = 0.0f;
+        for (int j = 0; j < qk; j++) {
+            const float av = fabsf(x[j]);
+            if (av > amax) amax = av;
+        }
+
+        const float d = amax / 127.0f;
+        const float id = d ? 1.0f / d : 0.0f;
+        const uint16_t hd = ds4q_f32_to_f16(d);
+        memcpy(out, &hd, sizeof(hd));
+
+        int8_t *qs = (int8_t *)(out + sizeof(hd));
+        for (int j = 0; j < qk; j++) qs[j] = (int8_t)roundf(x[j] * id);
+        out += sizeof(hd) + qk;
+    }
+    return (size_t)nrows * row_size;
+}
+
+static void ds4q_write_q4_k_block_ref(const float *x, uint8_t *y) {
+    enum { scales_off = 4, qs_off = 16 };
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    float weights[32];
+    float mins[QK_K / 32];
+    float scales[QK_K / 32];
+    uint8_t *scales_out = y + scales_off;
+    uint8_t *qs_out = y + qs_off;
+
+    float max_scale = 0;
+    float max_min = 0;
+    for (int j = 0; j < QK_K / 32; j++) {
+        float sum_x2 = 0;
+        for (int l = 0; l < 32; l++) sum_x2 += x[32 * j + l] * x[32 * j + l];
+        float av_x = sqrtf(sum_x2 / 32);
+        for (int l = 0; l < 32; l++) weights[l] = av_x + fabsf(x[32 * j + l]);
+        scales[j] = ds4q_make_qkx2_quants(32, 15, x + 32 * j, weights, L + 32 * j,
+                                           &mins[j], Laux, -1.0f, 0.1f, 20, false);
+        if (scales[j] > max_scale) max_scale = scales[j];
+        if (mins[j] > max_min) max_min = mins[j];
+    }
+
+    float inv_scale = max_scale > 0 ? 63.0f / max_scale : 0.0f;
+    float inv_min = max_min > 0 ? 63.0f / max_min : 0.0f;
+    for (int j = 0; j < QK_K / 32; j++) {
+        uint8_t ls = ds4q_nearest_int(inv_scale * scales[j]);
+        uint8_t lm = ds4q_nearest_int(inv_min * mins[j]);
+        ls = DS4Q_MIN(63, ls);
+        lm = DS4Q_MIN(63, lm);
+        if (j < 4) {
+            scales_out[j] = ls;
+            scales_out[j + 4] = lm;
+        } else {
+            scales_out[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
+            scales_out[j - 4] |= ((ls >> 4) << 6);
+            scales_out[j - 0] |= ((lm >> 4) << 6);
+        }
+    }
+
+    uint16_t d = ds4q_f32_to_f16(max_scale / 63.0f);
+    uint16_t dmin = ds4q_f32_to_f16(max_min / 63.0f);
+    memcpy(y + 0, &d, sizeof(d));
+    memcpy(y + 2, &dmin, sizeof(dmin));
+
+    uint8_t sc, m;
+    for (int j = 0; j < QK_K / 32; j++) {
+        ds4q_get_scale_min_k4(j, scales_out, &sc, &m);
+        const float dd = ds4q_f16_to_f32(d) * sc;
+        if (!dd) continue;
+        const float dm = ds4q_f16_to_f32(dmin) * m;
+        for (int ii = 0; ii < 32; ii++) {
+            int l = ds4q_nearest_int((x[32 * j + ii] + dm) / dd);
+            l = DS4Q_MAX(0, DS4Q_MIN(15, l));
+            L[32 * j + ii] = l;
+        }
+    }
+
+    uint8_t *q = qs_out;
+    for (int j = 0; j < QK_K; j += 64) {
+        for (int l = 0; l < 32; l++) q[l] = L[j + l] | (L[j + l + 32] << 4);
+        q += 32;
+    }
+}
+
+static void ds4q_write_q4_k_block_weighted(const float *x, uint8_t *y, const float *quant_weights) {
+    enum { scales_off = 4, qs_off = 16 };
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    uint8_t Ls[QK_K / 32];
+    uint8_t Lm[QK_K / 32];
+    float weights[32];
+    float sw[QK_K / 32];
+    float mins[QK_K / 32];
+    float scales[QK_K / 32];
+    uint8_t *scales_out = y + scales_off;
+    uint8_t *qs_out = y + qs_off;
+
+    float sum_x2 = 0;
+    for (int l = 0; l < QK_K; l++) sum_x2 += x[l] * x[l];
+    float sigma2 = 2 * sum_x2 / QK_K;
+    float av_x = sqrtf(sigma2);
+
+    for (int j = 0; j < QK_K / 32; j++) {
+        if (quant_weights) {
+            const float *qw = quant_weights + 32 * j;
+            for (int l = 0; l < 32; l++) weights[l] = qw[l] * sqrtf(sigma2 + x[32 * j + l] * x[32 * j + l]);
+        } else {
+            for (int l = 0; l < 32; l++) weights[l] = av_x + fabsf(x[32 * j + l]);
+        }
+        float sumw = 0;
+        for (int l = 0; l < 32; l++) sumw += weights[l];
+        sw[j] = sumw;
+        scales[j] = ds4q_make_qkx3_quants(32, 15, x + 32 * j, weights, L + 32 * j,
+                                           &mins[j], Laux, -0.9f, 0.05f, 36, false);
+    }
+
+    float d_block = ds4q_make_qp_quants(QK_K / 32, 63, scales, Ls, sw);
+    float m_block = ds4q_make_qp_quants(QK_K / 32, 63, mins, Lm, sw);
+    for (int j = 0; j < QK_K / 32; j++) {
+        uint8_t ls = Ls[j];
+        uint8_t lm = Lm[j];
+        if (j < 4) {
+            scales_out[j] = ls;
+            scales_out[j + 4] = lm;
+        } else {
+            scales_out[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
+            scales_out[j - 4] |= ((ls >> 4) << 6);
+            scales_out[j - 0] |= ((lm >> 4) << 6);
+        }
+    }
+
+    uint16_t d = ds4q_f32_to_f16(d_block);
+    uint16_t dmin = ds4q_f32_to_f16(m_block);
+    memcpy(y + 0, &d, sizeof(d));
+    memcpy(y + 2, &dmin, sizeof(dmin));
+
+    uint8_t sc, m;
+    for (int j = 0; j < QK_K / 32; j++) {
+        ds4q_get_scale_min_k4(j, scales_out, &sc, &m);
+        const float dd = ds4q_f16_to_f32(d) * sc;
+        if (!dd) continue;
+        const float dm = ds4q_f16_to_f32(dmin) * m;
+        for (int ii = 0; ii < 32; ii++) {
+            int l = ds4q_nearest_int((x[32 * j + ii] + dm) / dd);
+            l = DS4Q_MAX(0, DS4Q_MIN(15, l));
+            L[32 * j + ii] = l;
+        }
+    }
+
+    uint8_t *q = qs_out;
+    for (int j = 0; j < QK_K; j += 64) {
+        for (int l = 0; l < 32; l++) q[l] = L[j + l] | (L[j + l + 32] << 4);
+        q += 32;
+    }
+}
+
+static size_t ds4q_quantize_q4_k(const float *src, void *dst, int64_t start,
+                                 int64_t nrows, int64_t ncols, const float *quant_weights) {
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_Q4_K, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t blocks_per_row = ncols / QK_K;
+
+    for (int64_t row = 0; row < nrows; row++) {
+        const float *xrow = src + start + (size_t)row * (size_t)ncols;
+        for (int64_t b = 0; b < blocks_per_row; b++) {
+            uint8_t *block = out + (size_t)row * row_size + (size_t)b * ds4q_type_traits[DS4Q_TYPE_Q4_K].type_size;
+            const float *x = xrow + (size_t)b * QK_K;
+            if (quant_weights) {
+                ds4q_write_q4_k_block_weighted(x, block, quant_weights + (size_t)b * QK_K);
+            } else {
+                ds4q_write_q4_k_block_ref(x, block);
+            }
+        }
+    }
+    return (size_t)nrows * row_size;
+}
+
+static void ds4q_write_q5_k_block_ref(const float *x, uint8_t *y) {
+    enum { d_off = 0, dmin_off = 2, scales_off = 4, qh_off = 16, qs_off = 48 };
+    uint8_t L[QK_K];
+    uint8_t Laux[32];
+    float weights[32];
+    float mins[QK_K / 32];
+    float scales[QK_K / 32];
+    uint8_t *scales_out = y + scales_off;
+    uint8_t *qh = y + qh_off;
+    uint8_t *ql = y + qs_off;
+
+    float max_scale = 0.0f;
+    float max_min = 0.0f;
+    for (int j = 0; j < QK_K / 32; j++) {
+        float sum_x2 = 0.0f;
+        for (int l = 0; l < 32; l++) sum_x2 += x[32 * j + l] * x[32 * j + l];
+        const float av_x = sqrtf(sum_x2 / 32);
+        for (int l = 0; l < 32; l++) weights[l] = av_x + fabsf(x[32 * j + l]);
+        scales[j] = ds4q_make_qkx2_quants(
+            32, 31, x + 32 * j, weights, L + 32 * j, &mins[j], Laux,
+            -0.5f, 0.1f, 15, false);
+        if (scales[j] > max_scale) max_scale = scales[j];
+        if (mins[j] > max_min) max_min = mins[j];
+    }
+
+    const float inv_scale = max_scale > 0 ? 63.0f / max_scale : 0.0f;
+    const float inv_min = max_min > 0 ? 63.0f / max_min : 0.0f;
+    for (int j = 0; j < QK_K / 32; j++) {
+        uint8_t ls = DS4Q_MIN(63, ds4q_nearest_int(inv_scale * scales[j]));
+        uint8_t lm = DS4Q_MIN(63, ds4q_nearest_int(inv_min * mins[j]));
+        if (j < 4) {
+            scales_out[j] = ls;
+            scales_out[j + 4] = lm;
+        } else {
+            scales_out[j + 4] = (ls & 0xF) | ((lm & 0xF) << 4);
+            scales_out[j - 4] |= (ls >> 4) << 6;
+            scales_out[j] |= (lm >> 4) << 6;
+        }
+    }
+
+    const uint16_t d = ds4q_f32_to_f16(max_scale / 63.0f);
+    const uint16_t dmin = ds4q_f32_to_f16(max_min / 63.0f);
+    memcpy(y + d_off, &d, sizeof(d));
+    memcpy(y + dmin_off, &dmin, sizeof(dmin));
+
+    uint8_t sc, m;
+    for (int j = 0; j < QK_K / 32; j++) {
+        ds4q_get_scale_min_k4(j, scales_out, &sc, &m);
+        const float dd = ds4q_f16_to_f32(d) * sc;
+        if (!dd) continue;
+        const float dm = ds4q_f16_to_f32(dmin) * m;
+        for (int ii = 0; ii < 32; ii++) {
+            int l = ds4q_nearest_int((x[32 * j + ii] + dm) / dd);
+            L[32 * j + ii] = DS4Q_MAX(0, DS4Q_MIN(31, l));
+        }
+    }
+
+    memset(qh, 0, QK_K / 8);
+    uint8_t m1 = 1;
+    uint8_t m2 = 2;
+    for (int n = 0; n < QK_K; n += 64) {
+        for (int j = 0; j < 32; j++) {
+            int l1 = L[n + j];
+            if (l1 > 15) {
+                l1 -= 16;
+                qh[j] |= m1;
+            }
+            int l2 = L[n + j + 32];
+            if (l2 > 15) {
+                l2 -= 16;
+                qh[j] |= m2;
+            }
+            ql[j] = l1 | (l2 << 4);
+        }
+        m1 <<= 2;
+        m2 <<= 2;
+        ql += 32;
+    }
+}
+
+static size_t ds4q_quantize_q5_k(const float *src, void *dst, int64_t start,
+                                 int64_t nrows, int64_t ncols) {
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_Q5_K, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t blocks_per_row = ncols / QK_K;
+    for (int64_t row = 0; row < nrows; row++) {
+        const float *xrow = src + start + (size_t)row * (size_t)ncols;
+        for (int64_t b = 0; b < blocks_per_row; b++) {
+            uint8_t *block = out + (size_t)row * row_size +
+                (size_t)b * ds4q_type_traits[DS4Q_TYPE_Q5_K].type_size;
+            ds4q_write_q5_k_block_ref(xrow + (size_t)b * QK_K, block);
+        }
+    }
+    return (size_t)nrows * row_size;
+}
+
+static void ds4q_write_q6_k_block_ref(const float *x, uint8_t *y) {
+    enum { ql_off = 0, qh_off = 128, scales_off = 192, d_off = 208 };
+    int8_t L[QK_K];
+    float scales[QK_K / 16];
+    uint8_t *ql = y + ql_off;
+    uint8_t *qh = y + qh_off;
+    int8_t *scales_out = (int8_t *)(y + scales_off);
+
+    float max_scale = 0.0f;
+    float max_abs_scale = 0.0f;
+    for (int ib = 0; ib < QK_K / 16; ib++) {
+        const float scale = ds4q_make_qx_quants(16, 32, x + 16 * ib, L + 16 * ib, 1, NULL);
+        scales[ib] = scale;
+        const float abs_scale = fabsf(scale);
+        if (abs_scale > max_abs_scale) {
+            max_abs_scale = abs_scale;
+            max_scale = scale;
+        }
+    }
+    if (max_abs_scale < DS4Q_GROUP_MAX_EPS) {
+        memset(y, 0, ds4q_type_traits[DS4Q_TYPE_Q6_K].type_size);
+        return;
+    }
+
+    const float iscale = -128.0f / max_scale;
+    const uint16_t d = ds4q_f32_to_f16(1.0f / iscale);
+    memcpy(y + d_off, &d, sizeof(d));
+    for (int ib = 0; ib < QK_K / 16; ib++) {
+        scales_out[ib] = DS4Q_MIN(127, ds4q_nearest_int(iscale * scales[ib]));
+    }
+    for (int j = 0; j < QK_K / 16; j++) {
+        const float dd = ds4q_f16_to_f32(d) * scales_out[j];
+        if (!dd) continue;
+        for (int ii = 0; ii < 16; ii++) {
+            int l = ds4q_nearest_int(x[16 * j + ii] / dd);
+            l = DS4Q_MAX(-32, DS4Q_MIN(31, l));
+            L[16 * j + ii] = l + 32;
+        }
+    }
+    for (int j = 0; j < QK_K; j += 128) {
+        for (int l = 0; l < 32; l++) {
+            const uint8_t q1 = L[j + l] & 0xF;
+            const uint8_t q2 = L[j + l + 32] & 0xF;
+            const uint8_t q3 = L[j + l + 64] & 0xF;
+            const uint8_t q4 = L[j + l + 96] & 0xF;
+            ql[l] = q1 | (q3 << 4);
+            ql[l + 32] = q2 | (q4 << 4);
+            qh[l] = (L[j + l] >> 4) |
+                    ((L[j + l + 32] >> 4) << 2) |
+                    ((L[j + l + 64] >> 4) << 4) |
+                    ((L[j + l + 96] >> 4) << 6);
+        }
+        ql += 64;
+        qh += 32;
+    }
+}
+
+static size_t ds4q_quantize_q6_k(const float *src, void *dst, int64_t start,
+                                 int64_t nrows, int64_t ncols) {
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_Q6_K, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t blocks_per_row = ncols / QK_K;
+    for (int64_t row = 0; row < nrows; row++) {
+        const float *xrow = src + start + (size_t)row * (size_t)ncols;
+        for (int64_t b = 0; b < blocks_per_row; b++) {
+            uint8_t *block = out + (size_t)row * row_size +
+                (size_t)b * ds4q_type_traits[DS4Q_TYPE_Q6_K].type_size;
+            ds4q_write_q6_k_block_ref(xrow + (size_t)b * QK_K, block);
+        }
+    }
+    return (size_t)nrows * row_size;
+}
+
+static void ds4q_write_q2_k_block_ref(const float *x, uint8_t *y) {
+    enum { scales_off = 0, qs_off = 16, d_off = 80, dmin_off = 82 };
+    const float q4scale = 15.0f;
+    uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float weights[16];
+    float mins[QK_K / 16];
+    float scales[QK_K / 16];
+    uint8_t *scales_out = y + scales_off;
+    uint8_t *qs_out = y + qs_off;
+
+    float max_scale = 0;
+    float max_min = 0;
+    for (int j = 0; j < QK_K / 16; j++) {
+        for (int l = 0; l < 16; l++) weights[l] = fabsf(x[16 * j + l]);
+        scales[j] = ds4q_make_qkx2_quants(16, 3, x + 16 * j, weights, L + 16 * j,
+                                           &mins[j], Laux, -0.5f, 0.1f, 15, true);
+        if (scales[j] > max_scale) max_scale = scales[j];
+        if (mins[j] > max_min) max_min = mins[j];
+    }
+
+    uint16_t hd, hmin;
+    if (max_scale > 0) {
+        float iscale = q4scale / max_scale;
+        for (int j = 0; j < QK_K / 16; j++) scales_out[j] = ds4q_nearest_int(iscale * scales[j]);
+        hd = ds4q_f32_to_f16(max_scale / q4scale);
+    } else {
+        memset(scales_out, 0, QK_K / 16);
+        hd = ds4q_f32_to_f16(0.0f);
+    }
+    if (max_min > 0) {
+        float iscale = q4scale / max_min;
+        for (int j = 0; j < QK_K / 16; j++) scales_out[j] |= ds4q_nearest_int(iscale * mins[j]) << 4;
+        hmin = ds4q_f32_to_f16(max_min / q4scale);
+    } else {
+        hmin = ds4q_f32_to_f16(0.0f);
+    }
+    memcpy(y + d_off, &hd, sizeof(hd));
+    memcpy(y + dmin_off, &hmin, sizeof(hmin));
+
+    for (int j = 0; j < QK_K / 16; j++) {
+        const float d = ds4q_f16_to_f32(hd) * (scales_out[j] & 0xF);
+        if (!d) continue;
+        const float dm = ds4q_f16_to_f32(hmin) * (scales_out[j] >> 4);
+        for (int ii = 0; ii < 16; ii++) {
+            int l = ds4q_nearest_int((x[16 * j + ii] + dm) / d);
+            l = DS4Q_MAX(0, DS4Q_MIN(3, l));
+            L[16 * j + ii] = l;
+        }
+    }
+
+    for (int j = 0; j < QK_K; j += 128) {
+        for (int l = 0; l < 32; l++) {
+            qs_out[j / 4 + l] = L[j + l] | (L[j + l + 32] << 2) |
+                                (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+        }
+    }
+}
+
+static void ds4q_write_q2_k_block_weighted(const float *x, uint8_t *y, const float *quant_weights) {
+    enum { scales_off = 0, qs_off = 16, d_off = 80, dmin_off = 82 };
+    uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float mins[QK_K / 16];
+    float scales[QK_K / 16];
+    float sw[QK_K / 16];
+    float weight[16];
+    uint8_t Ls[QK_K / 16], Lm[QK_K / 16];
+    uint8_t *scales_out = y + scales_off;
+    uint8_t *qs_out = y + qs_off;
+
+    memset(sw, 0, sizeof(sw));
+    float sumx2 = 0;
+    for (int j = 0; j < QK_K; j++) sumx2 += x[j] * x[j];
+    float sigma2 = sumx2 / QK_K;
+    for (int j = 0; j < QK_K / 16; j++) {
+        const float *qw = quant_weights + 16 * j;
+        for (int l = 0; l < 16; l++) weight[l] = qw[l] * sqrtf(sigma2 + x[16 * j + l] * x[16 * j + l]);
+        for (int l = 0; l < QK_K / 16; l++) sw[j] += weight[l];
+        scales[j] = ds4q_make_qkx3_quants(16, 3, x + 16 * j, weight, L + 16 * j,
+                                           &mins[j], Laux, -0.9f, 0.05f, 36, false);
+    }
+
+    float dm = ds4q_make_qp_quants(QK_K / 16, 15, scales, Ls, sw);
+    float mm = ds4q_make_qp_quants(QK_K / 16, 15, mins, Lm, sw);
+    uint16_t hd = ds4q_f32_to_f16(dm);
+    uint16_t hmin = ds4q_f32_to_f16(mm);
+    memcpy(y + d_off, &hd, sizeof(hd));
+    memcpy(y + dmin_off, &hmin, sizeof(hmin));
+    dm = ds4q_f16_to_f32(hd);
+    mm = ds4q_f16_to_f32(hmin);
+
+    for (int j = 0; j < QK_K / 16; j++) scales_out[j] = Ls[j] | (Lm[j] << 4);
+
+    for (int j = 0; j < QK_K / 16; j++) {
+        const float d = dm * (scales_out[j] & 0xF);
+        if (!d) continue;
+        const float m = mm * (scales_out[j] >> 4);
+        for (int ii = 0; ii < 16; ii++) {
+            int l = ds4q_nearest_int((x[16 * j + ii] + m) / d);
+            l = DS4Q_MAX(0, DS4Q_MIN(3, l));
+            L[16 * j + ii] = l;
+        }
+    }
+
+    for (int j = 0; j < QK_K; j += 128) {
+        for (int l = 0; l < 32; l++) {
+            qs_out[j / 4 + l] = L[j + l] | (L[j + l + 32] << 2) |
+                                (L[j + l + 64] << 4) | (L[j + l + 96] << 6);
+        }
+    }
+}
+
+static size_t ds4q_quantize_q2_k(const float *src, void *dst, int64_t start,
+                                 int64_t nrows, int64_t ncols, const float *quant_weights) {
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_Q2_K, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t blocks_per_row = ncols / QK_K;
+
+    for (int64_t row = 0; row < nrows; row++) {
+        const float *xrow = src + start + (size_t)row * (size_t)ncols;
+        for (int64_t b = 0; b < blocks_per_row; b++) {
+            uint8_t *block = out + (size_t)row * row_size + (size_t)b * ds4q_type_traits[DS4Q_TYPE_Q2_K].type_size;
+            const float *x = xrow + (size_t)b * QK_K;
+            if (quant_weights) {
+                ds4q_write_q2_k_block_weighted(x, block, quant_weights + (size_t)b * QK_K);
+            } else {
+                ds4q_write_q2_k_block_ref(x, block);
+            }
+        }
+    }
+    return (size_t)nrows * row_size;
+}
+
+typedef struct {
+    uint64_t *grid;
+    int *map;
+    uint16_t *neighbours;
+} ds4q_iq2_data;
+
+static ds4q_iq2_data ds4q_iq2_xxs_data;
+
+static int ds4q_iq2_compare_func(const void *left, const void *right) {
+    const int *l = (const int *)left;
+    const int *r = (const int *)right;
+    return l[0] < r[0] ? -1 :
+           l[0] > r[0] ?  1 :
+           l[1] < r[1] ? -1 :
+           l[1] > r[1] ?  1 : 0;
+}
+
+/*
+ * IQ2_XXS quantizes a 256-value row block as eight 32-value groups.  Each
+ * group stores four 8-value grid indices plus four 7-bit sign masks; the
+ * single f16 block scale is refined by 4-bit per-group scale nibbles.
+ *
+ * The grid is tiny, but not every possible 2-bit 8-tuple is allowed.  During
+ * initialization we build the direct map for allowed tuples and a nearest-grid
+ * list for the missing ones, matching the GGML search exactly.
+ */
+static void ds4q_iq2_xxs_init(void) {
+    if (ds4q_iq2_xxs_data.grid) return;
+    pthread_mutex_lock(&ds4q_init_mutex);
+    if (ds4q_iq2_xxs_data.grid) {
+        pthread_mutex_unlock(&ds4q_init_mutex);
+        return;
+    }
+
+    enum { grid_size = 256, map_size = 43692, neighbour_shells = 2 };
+    static const uint16_t kgrid[256] = {
+            0,     2,     5,     8,    10,    17,    20,    32,    34,    40,    42,    65,    68,    80,    88,    97,
+          100,   128,   130,   138,   162,   257,   260,   272,   277,   320,   388,   408,   512,   514,   546,   642,
+         1025,  1028,  1040,  1057,  1060,  1088,  1090,  1096,  1120,  1153,  1156,  1168,  1188,  1280,  1282,  1288,
+         1312,  1350,  1385,  1408,  1425,  1545,  1552,  1600,  1668,  1700,  2048,  2053,  2056,  2068,  2088,  2113,
+         2116,  2128,  2130,  2184,  2308,  2368,  2562,  2580,  4097,  4100,  4112,  4129,  4160,  4192,  4228,  4240,
+         4245,  4352,  4360,  4384,  4432,  4442,  4480,  4644,  4677,  5120,  5128,  5152,  5157,  5193,  5248,  5400,
+         5474,  5632,  5654,  6145,  6148,  6160,  6208,  6273,  6400,  6405,  6560,  6737,  8192,  8194,  8202,  8260,
+         8289,  8320,  8322,  8489,  8520,  8704,  8706,  9217,  9220,  9232,  9280,  9302,  9472,  9537,  9572,  9872,
+        10248, 10272, 10388, 10820, 16385, 16388, 16400, 16408, 16417, 16420, 16448, 16456, 16470, 16480, 16513, 16516,
+        16528, 16640, 16672, 16737, 16768, 16773, 16897, 16912, 16968, 16982, 17000, 17408, 17416, 17440, 17536, 17561,
+        17682, 17700, 17920, 18433, 18436, 18448, 18496, 18501, 18688, 18776, 18785, 18818, 19013, 19088, 20480, 20488,
+        20497, 20505, 20512, 20608, 20616, 20740, 20802, 20900, 21137, 21648, 21650, 21770, 22017, 22100, 22528, 22545,
+        22553, 22628, 22848, 23048, 24580, 24592, 24640, 24680, 24832, 24917, 25112, 25184, 25600, 25605, 25872, 25874,
+        25988, 26690, 32768, 32770, 32778, 32833, 32898, 33028, 33048, 33088, 33297, 33793, 33796, 33808, 33813, 33856,
+        33888, 34048, 34118, 34196, 34313, 34368, 34400, 34818, 35076, 35345, 36868, 36880, 36900, 36928, 37025, 37142,
+        37248, 37445, 37888, 37922, 37956, 38225, 39041, 39200, 40962, 41040, 41093, 41225, 41472, 42008, 43088, 43268,
+    };
+
+    uint64_t *grid = malloc((size_t)grid_size * sizeof(grid[0]));
+    int *map = malloc((size_t)map_size * sizeof(map[0]));
+    int *dist2 = malloc((size_t)2 * grid_size * sizeof(dist2[0]));
+    assert(grid && map && dist2);
+
+    for (int k = 0; k < grid_size; k++) {
+        int8_t *pos = (int8_t *)(grid + k);
+        for (int i = 0; i < 8; i++) {
+            int l = (kgrid[k] >> (2 * i)) & 3;
+            pos[i] = 2 * l + 1;
+        }
+    }
+
+    for (int i = 0; i < map_size; i++) map[i] = -1;
+    for (int i = 0; i < grid_size; i++) map[kgrid[i]] = i;
+
+    int8_t pos[8];
+    int num_neighbors = 0;
+    int num_not_in_map = 0;
+    for (int i = 0; i < map_size; i++) {
+        if (map[i] >= 0) continue;
+        num_not_in_map++;
+        for (int k = 0; k < 8; k++) pos[k] = 2 * ((i >> (2 * k)) & 3) + 1;
+        for (int j = 0; j < grid_size; j++) {
+            const int8_t *pg = (const int8_t *)(grid + j);
+            int d2 = 0;
+            for (int k = 0; k < 8; k++) d2 += (pg[k] - pos[k]) * (pg[k] - pos[k]);
+            dist2[2 * j + 0] = d2;
+            dist2[2 * j + 1] = j;
+        }
+        qsort(dist2, grid_size, 2 * sizeof(int), ds4q_iq2_compare_func);
+        int d2 = dist2[0], have = 1;
+        for (int j = 0; j < grid_size; j++) {
+            if (dist2[2 * j] > d2) {
+                if (have == neighbour_shells) break;
+                d2 = dist2[2 * j];
+                have++;
+            }
+            num_neighbors++;
+        }
+    }
+
+    uint16_t *neighbours = malloc((size_t)(num_neighbors + num_not_in_map) * sizeof(neighbours[0]));
+    assert(neighbours);
+    int counter = 0;
+    for (int i = 0; i < map_size; i++) {
+        if (map[i] >= 0) continue;
+        for (int k = 0; k < 8; k++) pos[k] = 2 * ((i >> (2 * k)) & 3) + 1;
+        for (int j = 0; j < grid_size; j++) {
+            const int8_t *pg = (const int8_t *)(grid + j);
+            int d2 = 0;
+            for (int k = 0; k < 8; k++) d2 += (pg[k] - pos[k]) * (pg[k] - pos[k]);
+            dist2[2 * j + 0] = d2;
+            dist2[2 * j + 1] = j;
+        }
+        qsort(dist2, grid_size, 2 * sizeof(int), ds4q_iq2_compare_func);
+        map[i] = -(counter + 1);
+        int d2 = dist2[0], have = 1;
+        uint16_t *start = &neighbours[counter++];
+        int n = 0;
+        for (int j = 0; j < grid_size; j++) {
+            if (dist2[2 * j] > d2) {
+                if (have == neighbour_shells) break;
+                d2 = dist2[2 * j];
+                have++;
+            }
+            neighbours[counter++] = (uint16_t)dist2[2 * j + 1];
+            n++;
+        }
+        *start = (uint16_t)n;
+    }
+
+    free(dist2);
+    ds4q_iq2_xxs_data.map = map;
+    ds4q_iq2_xxs_data.neighbours = neighbours;
+    ds4q_iq2_xxs_data.grid = grid;
+    pthread_mutex_unlock(&ds4q_init_mutex);
+}
+
+static int ds4q_iq2_find_best_neighbour(const uint16_t *neighbours, const uint64_t *grid,
+                                        const float *xval, const float *weight,
+                                        float scale, uint8_t *L) {
+    int num_neighbors = neighbours[0];
+    assert(num_neighbors > 0);
+    float best_d2 = FLT_MAX;
+    int grid_index = -1;
+    for (int j = 1; j <= num_neighbors; j++) {
+        const int8_t *pg = (const int8_t *)(grid + neighbours[j]);
+        float d2 = 0;
+        for (int i = 0; i < 8; i++) {
+            float q = pg[i];
+            float diff = scale * q - xval[i];
+            d2 += weight[i] * diff * diff;
+        }
+        if (d2 < best_d2) {
+            best_d2 = d2;
+            grid_index = neighbours[j];
+        }
+    }
+    assert(grid_index >= 0);
+    const int8_t *pg = (const int8_t *)(grid + grid_index);
+    for (int i = 0; i < 8; i++) L[i] = (uint8_t)((pg[i] - 1) / 2);
+    return grid_index;
+}
+
+static void ds4q_write_iq2_xxs_block(const float *x, uint8_t *y, const float *quant_weights) {
+    enum { d_off = 0, qs_off = 2, block_size = 32, k_max_q = 3 };
+    assert(quant_weights);
+
+    uint32_t q2[2 * (QK_K / block_size)];
+    float scales[QK_K / block_size];
+    float weight[block_size];
+    float xval[block_size];
+    uint8_t L[block_size];
+    uint8_t Laux[block_size];
+    float waux[block_size];
+    uint8_t block_signs[4];
+
+    uint16_t hd = ds4q_f32_to_f16(0.0f);
+    memcpy(y + d_off, &hd, sizeof(hd));
+    memset(q2, 0, sizeof(q2));
+
+    const uint64_t *grid = ds4q_iq2_xxs_data.grid;
+    const int *map = ds4q_iq2_xxs_data.map;
+    const uint16_t *neighbours = ds4q_iq2_xxs_data.neighbours;
+    assert(grid && map && neighbours);
+
+    float sumx2 = 0;
+    for (int i = 0; i < QK_K; i++) sumx2 += x[i] * x[i];
+    float sigma2 = sumx2 / QK_K;
+    float max_scale = 0;
+
+    for (int ib = 0; ib < QK_K / block_size; ib++) {
+        const float *xb = x + block_size * ib;
+        const float *qw = quant_weights + block_size * ib;
+        for (int i = 0; i < block_size; i++) {
+            weight[i] = qw[i] * sqrtf(sigma2 + xb[i] * xb[i]);
+            waux[i] = sqrtf(weight[i]);
+        }
+        for (int k = 0; k < 4; k++) {
+            int nflip = 0;
+            uint8_t s = 0;
+            for (int i = 0; i < 8; i++) {
+                float v = xb[8 * k + i];
+                if (v >= 0) {
+                    xval[8 * k + i] = v;
+                } else {
+                    xval[8 * k + i] = -v;
+                    nflip++;
+                    s |= (uint8_t)(1u << i);
+                }
+            }
+            if (nflip % 2) {
+                int imin = 0;
+                float min = weight[8 * k] * xb[8 * k] * xb[8 * k];
+                for (int i = 1; i < 8; i++) {
+                    float ax = weight[8 * k + i] * xb[8 * k + i] * xb[8 * k + i];
+                    if (ax < min) {
+                        min = ax;
+                        imin = i;
+                    }
+                }
+                xval[8 * k + imin] = -xval[8 * k + imin];
+                s ^= (uint8_t)(1u << imin);
+            }
+            block_signs[k] = s & 127;
+        }
+
+        float max = xval[0];
+        for (int i = 1; i < block_size; i++) max = DS4Q_MAX(max, xval[i]);
+        if (max < DS4Q_GROUP_MAX_EPS) {
+            scales[ib] = 0;
+            memset(L, 0, sizeof(L));
+            continue;
+        }
+
+        float scale = ds4q_make_qp_quants(block_size, k_max_q + 1, xval, L, weight);
+        float eff_max = scale * k_max_q;
+        if (eff_max <= 0) {
+            scales[ib] = 0;
+            memset(L, 0, sizeof(L));
+            continue;
+        }
+
+        float best = 0;
+        for (int is = -6; is <= 6; is++) {
+            float id = (2 * k_max_q - 1 + is * 0.1f) / eff_max;
+            float this_scale = 1 / id;
+            for (int k = 0; k < 4; k++) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; i++) {
+                    int l = ds4q_nearest_int(0.5f * (id * xval[8 * k + i] - 1));
+                    l = DS4Q_MAX(0, DS4Q_MIN(k_max_q - 1, l));
+                    Laux[8 * k + i] = (uint8_t)l;
+                    u |= (uint16_t)(l << (2 * i));
+                }
+                int grid_index = map[u];
+                if (grid_index < 0) {
+                    const uint16_t *nbs = neighbours - map[u] - 1;
+                    ds4q_iq2_find_best_neighbour(nbs, grid, xval + 8 * k, waux + 8 * k,
+                                                 this_scale, Laux + 8 * k);
+                }
+            }
+            float sumqx = 0, sumq2 = 0;
+            for (int i = 0; i < block_size; i++) {
+                float w = weight[i];
+                float q = 2 * Laux[i] + 1;
+                sumqx += w * xval[i] * q;
+                sumq2 += w * q * q;
+            }
+            if (sumq2 > 0 && sumqx * sumqx > best * sumq2) {
+                scale = sumqx / sumq2;
+                best = scale * sumqx;
+                memcpy(L, Laux, sizeof(L));
+            }
+        }
+
+        if (scale > 0) {
+            float id = 1 / scale;
+            for (int k = 0; k < 4; k++) {
+                uint16_t u = 0;
+                for (int i = 0; i < 8; i++) {
+                    int l = ds4q_nearest_int(0.5f * (id * xval[8 * k + i] - 1));
+                    l = DS4Q_MAX(0, DS4Q_MIN(k_max_q - 1, l));
+                    u |= (uint16_t)(l << (2 * i));
+                }
+                int grid_index = map[u];
+                if (grid_index < 0) {
+                    const uint16_t *nbs = neighbours - map[u] - 1;
+                    grid_index = ds4q_iq2_find_best_neighbour(nbs, grid, xval + 8 * k,
+                                                              waux + 8 * k, scale, L + 8 * k);
+                }
+                const int8_t *pg = (const int8_t *)(grid + grid_index);
+                for (int i = 0; i < 8; i++) L[8 * k + i] = (uint8_t)((pg[i] - 1) / 2);
+            }
+            float sumqx = 0, sumq2 = 0;
+            for (int i = 0; i < block_size; i++) {
+                float w = weight[i];
+                float q = 2 * L[i] + 1;
+                sumqx += w * xval[i] * q;
+                sumq2 += w * q * q;
+            }
+            if (sumq2 > 0) scale = sumqx / sumq2;
+        }
+
+        if (scale < 0) {
+            scale = -scale;
+            for (int k = 0; k < 4; k++) block_signs[k] = (~block_signs[k]) & 127;
+        }
+
+        for (int k = 0; k < 4; k++) {
+            uint16_t u = 0;
+            for (int i = 0; i < 8; i++) u |= (uint16_t)(L[8 * k + i] << (2 * i));
+            int grid_index = map[u];
+            assert(grid_index >= 0);
+            q2[2 * ib + 0] |= (uint32_t)grid_index << (8 * k);
+            q2[2 * ib + 1] |= (uint32_t)block_signs[k] << (7 * k);
+        }
+        assert(scale >= 0);
+        scales[ib] = scale;
+        max_scale = DS4Q_MAX(max_scale, scale);
+    }
+
+    if (!max_scale) {
+        memset(y + qs_off, 0, QK_K / 4);
+        return;
+    }
+
+    float d = max_scale / 31;
+    hd = ds4q_f32_to_f16(d);
+    memcpy(y + d_off, &hd, sizeof(hd));
+    float id = 1 / d;
+    for (int ib = 0; ib < QK_K / block_size; ib++) {
+        int l = ds4q_nearest_int(0.5f * (id * scales[ib] - 1));
+        l = DS4Q_MAX(0, DS4Q_MIN(15, l));
+        q2[2 * ib + 1] |= (uint32_t)l << 28;
+    }
+    memcpy(y + qs_off, q2, QK_K / 4);
+}
+
+static size_t ds4q_quantize_iq2_xxs(const float *src, void *dst, int64_t start,
+                                    int64_t nrows, int64_t ncols, const float *quant_weights) {
+    assert(quant_weights);
+    ds4q_iq2_xxs_init();
+    const size_t row_size = ds4q_row_size(DS4Q_TYPE_IQ2_XXS, ncols);
+    const int64_t start_row = start / ncols;
+    uint8_t *out = (uint8_t *)dst + (size_t)start_row * row_size;
+    const int64_t blocks_per_row = ncols / QK_K;
+
+    for (int64_t row = 0; row < nrows; row++) {
+        const float *xrow = src + start + (size_t)row * (size_t)ncols;
+        for (int64_t b = 0; b < blocks_per_row; b++) {
+            uint8_t *block = out + (size_t)row * row_size + (size_t)b * ds4q_type_traits[DS4Q_TYPE_IQ2_XXS].type_size;
+            ds4q_write_iq2_xxs_block(xrow + (size_t)b * QK_K, block,
+                                     quant_weights + (size_t)b * QK_K);
+        }
+    }
+    return (size_t)nrows * row_size;
+}
+
+const char *ds4q_type_name(ds4q_type type) {
+    if (type < 0 || type >= DS4Q_TYPE_COUNT) return NULL;
+    return ds4q_type_traits[type].name;
+}
+
+bool ds4q_can_quantize(ds4q_type type) {
+    if (type < 0 || type >= DS4Q_TYPE_COUNT) return false;
+    return ds4q_type_traits[type].can_quantize;
+}
+
+int64_t ds4q_block_size(ds4q_type type) {
+    if (type < 0 || type >= DS4Q_TYPE_COUNT) return 0;
+    return ds4q_type_traits[type].block_size;
+}
+
+size_t ds4q_row_size(ds4q_type type, int64_t ne) {
+    if (type < 0 || type >= DS4Q_TYPE_COUNT) return 0;
+    const ds4q_traits *tr = &ds4q_type_traits[type];
+    if (tr->block_size <= 0 || tr->type_size == 0 || ne % tr->block_size != 0) return 0;
+    return tr->type_size * (size_t)(ne / tr->block_size);
+}
+
+bool ds4q_requires_imatrix(ds4q_type type) {
+    if (type < 0 || type >= DS4Q_TYPE_COUNT) return false;
+    return ds4q_type_traits[type].requires_imatrix;
+}
+
+void ds4q_quantize_init(ds4q_type type) {
+    if (type == DS4Q_TYPE_IQ2_XXS) {
+        ds4q_iq2_xxs_init();
+    }
+}
+
+size_t ds4q_quantize_chunk(ds4q_type type, const float *src, void *dst,
+                           int64_t start, int64_t nrows, int64_t ncols,
+                           const float *imatrix) {
+    if (type == DS4Q_TYPE_Q8_0) {
+        (void)imatrix;
+        return ds4q_quantize_q8_0(src, dst, start, nrows, ncols);
+    }
+    if (type == DS4Q_TYPE_Q2_K) {
+        return ds4q_quantize_q2_k(src, dst, start, nrows, ncols, imatrix);
+    }
+    if (type == DS4Q_TYPE_Q4_K) {
+        return ds4q_quantize_q4_k(src, dst, start, nrows, ncols, imatrix);
+    }
+    if (type == DS4Q_TYPE_Q5_K) {
+        (void)imatrix;
+        return ds4q_quantize_q5_k(src, dst, start, nrows, ncols);
+    }
+    if (type == DS4Q_TYPE_Q6_K) {
+        (void)imatrix;
+        return ds4q_quantize_q6_k(src, dst, start, nrows, ncols);
+    }
+    if (type == DS4Q_TYPE_IQ2_XXS) {
+        return ds4q_quantize_iq2_xxs(src, dst, start, nrows, ncols, imatrix);
+    }
+    (void)src;
+    (void)dst;
+    (void)start;
+    (void)nrows;
+    (void)ncols;
+    (void)imatrix;
+    assert(!"unsupported DS4 quantization target");
+    return 0;
+}
+
+float ds4q_f16_to_f32(uint16_t bits) {
+    const uint32_t w = (uint32_t)bits << 16;
+    const uint32_t sign = w & UINT32_C(0x80000000);
+    const uint32_t two_w = w + w;
+    const uint32_t exp_offset = UINT32_C(0xE0) << 23;
+    const float exp_scale = 0x1.0p-112f;
+    const float normalized_value = ds4q_f32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+    const uint32_t magic_mask = UINT32_C(126) << 23;
+    const float magic_bias = 0.5f;
+    const float denormalized_value = ds4q_f32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+    const uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+    const uint32_t result = sign |
+        (two_w < denormalized_cutoff ? ds4q_f32_to_bits(denormalized_value) : ds4q_f32_to_bits(normalized_value));
+    return ds4q_f32_from_bits(result);
+}
+
+float ds4q_bf16_to_f32(uint16_t bits) {
+    return ds4q_f32_from_bits((uint32_t)bits << 16);
+}
+
+void ds4q_f32_to_f16_row(const float *src, uint16_t *dst, int64_t n) {
+    for (int64_t i = 0; i < n; i++) dst[i] = ds4q_f32_to_f16(src[i]);
+}
+
+void ds4q_f32_to_bf16_row(const float *src, uint16_t *dst, int64_t n) {
+    for (int64_t i = 0; i < n; i++) {
+        uint32_t bits = ds4q_f32_to_bits(src[i]);
+        if ((bits & UINT32_C(0x7fffffff)) > UINT32_C(0x7f800000)) {
+            dst[i] = (uint16_t)((bits >> 16) | 64);
+        } else {
+            dst[i] = (uint16_t)((bits + (UINT32_C(0x7fff) + ((bits >> 16) & 1))) >> 16);
+        }
+    }
+}

ds4/gguf-tools/quants.h

@@ -0,0 +1,75 @@
+#ifndef DS4_QUANTS_H
+#define DS4_QUANTS_H
+
+/*
+ * Narrow quantization API used by the DS4 GGUF writer.
+ *
+ * The enum values intentionally match GGUF/GGML type IDs so template metadata
+ * can be copied without translation.  Only the formats used by the DS4 Flash
+ * quantization recipes are implemented as output targets.
+ */
+
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#define DS4Q_MAX_DIMS 4
+
+typedef enum {
+    DS4Q_TYPE_F32     = 0,
+    DS4Q_TYPE_F16     = 1,
+    DS4Q_TYPE_Q4_0    = 2,
+    DS4Q_TYPE_Q4_1    = 3,
+    DS4Q_TYPE_Q5_0    = 6,
+    DS4Q_TYPE_Q5_1    = 7,
+    DS4Q_TYPE_Q8_0    = 8,
+    DS4Q_TYPE_Q8_1    = 9,
+    DS4Q_TYPE_Q2_K    = 10,
+    DS4Q_TYPE_Q3_K    = 11,
+    DS4Q_TYPE_Q4_K    = 12,
+    DS4Q_TYPE_Q5_K    = 13,
+    DS4Q_TYPE_Q6_K    = 14,
+    DS4Q_TYPE_Q8_K    = 15,
+    DS4Q_TYPE_IQ2_XXS = 16,
+    DS4Q_TYPE_IQ2_XS  = 17,
+    DS4Q_TYPE_IQ3_XXS = 18,
+    DS4Q_TYPE_IQ1_S   = 19,
+    DS4Q_TYPE_IQ4_NL  = 20,
+    DS4Q_TYPE_IQ3_S   = 21,
+    DS4Q_TYPE_IQ2_S   = 22,
+    DS4Q_TYPE_IQ4_XS  = 23,
+    DS4Q_TYPE_I8      = 24,
+    DS4Q_TYPE_I16     = 25,
+    DS4Q_TYPE_I32     = 26,
+    DS4Q_TYPE_I64     = 27,
+    DS4Q_TYPE_F64     = 28,
+    DS4Q_TYPE_IQ1_M   = 29,
+    DS4Q_TYPE_BF16    = 30,
+    DS4Q_TYPE_TQ1_0   = 34,
+    DS4Q_TYPE_TQ2_0   = 35,
+    DS4Q_TYPE_MXFP4   = 39,
+    DS4Q_TYPE_NVFP4   = 40,
+    DS4Q_TYPE_Q1_0    = 41,
+    DS4Q_TYPE_COUNT   = 42,
+} ds4q_type;
+
+static inline size_t ds4q_pad(size_t x, size_t n) {
+    return ((x + n - 1) / n) * n;
+}
+
+const char *ds4q_type_name(ds4q_type type);
+bool ds4q_can_quantize(ds4q_type type);
+int64_t ds4q_block_size(ds4q_type type);
+size_t ds4q_row_size(ds4q_type type, int64_t ne);
+bool ds4q_requires_imatrix(ds4q_type type);
+void ds4q_quantize_init(ds4q_type type);
+size_t ds4q_quantize_chunk(ds4q_type type, const float *src, void *dst,
+                           int64_t start, int64_t nrows, int64_t ncols,
+                           const float *imatrix);
+
+float ds4q_f16_to_f32(uint16_t bits);
+float ds4q_bf16_to_f32(uint16_t bits);
+void ds4q_f32_to_f16_row(const float *src, uint16_t *dst, int64_t n);
+void ds4q_f32_to_bf16_row(const float *src, uint16_t *dst, int64_t n);
+
+#endif

generation_config.json

@@ -0,0 +1,12 @@
+{
+    "bos_token_id": 248044,
+    "do_sample": true,
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "pad_token_id": 248044,
+    "temperature": 1.0,
+    "top_k": 20,
+    "top_p": 0.95
+}

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

reproduction/README.md

@@ -0,0 +1,66 @@
+# Swift1.5 Q5 conversion
+
+Source: `ukisai/Swift1.5-Qwen3.8-Flash-Next@0bd4fe22431372cdad1979267d3ab45aa7e6150a`.
+Recipe: published Qwen `MQ-Q5-SSD-PLE-BF16`, source-map SHA-256
+`bedb8b03cd6a06072c1e0b31eda42fc8be4642d22a5889ebd5da10d902fd3de9`.
+
+Keep `source/`, `work/` and this package under `publish/` in one parent directory.
+Install `requirements.txt` into `work/venv`. The release used CPU-only
+`torch==2.11.0+cpu`; install torch from `https://download.pytorch.org/whl/cpu`.
+
+```bash
+hf download ukisai/Swift1.5-Qwen3.8-Flash-Next \
+  --revision 0bd4fe22431372cdad1979267d3ab45aa7e6150a \
+  --local-dir "$root/source" --exclude '*.mp4' --exclude '*.png' --exclude 'benchmarks/*'
+```
+
+`manifests/swift/recipe-base-source-map.json` ports the reference tensor map
+without changing roles, shapes, slices or quantization targets. The planner
+reads the actual Swift integer PLE controls and verifies exact Q5/PLE-plan
+equivalence to the reference. The converter refuses to overwrite output GGUFs.
+
+Before copying PLE, run:
+
+```bash
+python publish/reproduction/scripts/verify_ple_identity.py \
+  --model-dir "$root/source" \
+  --reference publish/reproduction/manifests/reference/bf16-ple-manifest.json \
+  --reference-files publish/reproduction/manifests/reference/base-source-info.json \
+  --candidate-files publish/reproduction/manifests/swift/upstream-source-info.json \
+  --out publish/reproduction/manifests/swift/verify-ple-identity.json
+```
+
+The 31 complete PLE-only source shards are byte-identical to pinned Qwen BF16
+shards. The four PLE tensors in modified mixed shards are hashed directly.
+After all 128 parts pass, copy the existing Qwen FP8 sidecar:
+
+```bash
+cp -r ~/workspace/ds4-exaone/models/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF/PLE-FP8 \
+  "$root/publish/PLE-FP8"
+bash "$root/publish/reproduction/run.sh" "$root" build
+```
+
+`run.sh` verifies all selected source files and all 1,658 source tensors,
+builds the main map, checks exact recipe/PLE layout equivalence, verifies PLE
+identity and copied hashes, converts 1,628 main tensors, audits GGUF structure,
+independently hashes weights and writes the artifact manifest/card.
+
+Main GGUF metadata keeps the existing BF16 packaging contract. Runtime FP8
+selection requires `DS4_QWEN_PLE_DIR`; no BF16 PLE files are bundled here.
+There is no imatrix dependency. Source BF16 data remain mmap-backed.
+
+`publish` mode uploads to the fixed Baekpica repository after local gates,
+checks public visibility, all seven large-file LFS hashes/sizes, and fully
+reads/hashes every smaller staged file without authentication. It then
+publishes the verification receipt and verifies the final revision.
+
+Run focused packaging tests with:
+
+```bash
+cd publish/reproduction/scripts
+python -m unittest test_ple_identity test_release_manifest test_publication
+```
+
+Conversion checks do not qualify Swift GPU inference, serving, performance
+or task quality. Original Qwen FP8 extraction receipts are retained separately
+from the fresh package checks.

reproduction/converter/build_ssd_ple_manifests.py

@@ -0,0 +1,403 @@
+#!/usr/bin/env python3
+"""Build the audited Q5/Q6 main-GGUF map and BF16 SSD-PLE extraction map.
+
+The main GGUF intentionally omits the 128 PLE embedding tensors.  Their BF16
+payload is mapped into a small number of page-aligned raw sidecars, while the
+integer PLE controls remain in the main GGUF.  This script only plans the
+layout; ``extract_ple_sidecar.py`` materializes it and fills the checksums.
+"""
+
+from __future__ import annotations
+
+import argparse
+import collections
+import hashlib
+import json
+import math
+import re
+from pathlib import Path
+from typing import Any
+
+from safetensors import safe_open
+
+
+SOURCE_REPO = "ukisai/Swift1.5-Qwen3.8-Flash-Next"
+SOURCE_REVISION = "0bd4fe22431372cdad1979267d3ab45aa7e6150a"
+VARIANT = "MQ-Q5-SSD-PLE-BF16"
+BASELINE_VARIANT = "MQ-NG5-Q2K-Q4-SPLIT-E4"
+ALIGNMENT = 4096
+PHYSICAL_FILES = 4
+EXPECTED_PLE_PARTS = 128
+EXPECTED_ROWS_PER_PART = 2_500_012
+EXPECTED_ROW_WIDTH = 160
+EXPECTED_TOTAL_PARAMETERS = 179_999_981_459
+EXPECTED_MAIN_PARAMETERS = 128_799_735_699
+
+QUANT_LAYOUT: dict[str, tuple[int, int]] = {
+    "F32": (1, 4),
+    "BF16": (1, 2),
+    "I64": (1, 8),
+    "Q4_0": (32, 18),
+    "Q5_0": (32, 22),
+    "Q5_1": (32, 24),
+    "Q8_0": (32, 34),
+    "Q2_K": (256, 84),
+    "Q4_K": (256, 144),
+    "Q5_K": (256, 176),
+    "Q6_K": (256, 210),
+}
+
+CANONICAL_REGIONS = (
+    "51.2B-parameter PLE n-gram table (128 shards)",
+    "Routed expert gate/up, interior layers 2–45",
+    "Routed expert gate/up, edge layers 0, 1, 46, 47",
+    "Routed expert down, main 512 columns",
+    "Routed expert down, 128-column tail",
+    "MTP routed experts and most always-active matrices",
+    "Hyper-connection, non-quantizable convolution/vision tensors",
+    "Norms, gates, recurrent/control state",
+    "Integer PLE controls",
+)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--source-map", type=Path, required=True)
+    parser.add_argument("--model-dir", type=Path, required=True)
+    parser.add_argument("--out-dir", type=Path, required=True)
+    parser.add_argument("--physical-files", type=int, default=PHYSICAL_FILES)
+    return parser.parse_args()
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(16 << 20), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def align(value: int, alignment: int = ALIGNMENT) -> int:
+    return (value + alignment - 1) // alignment * alignment
+
+
+def parameters(row: dict[str, Any]) -> int:
+    return math.prod(int(value) for value in row["shape"])
+
+
+def storage_bytes(row: dict[str, Any], qtype: str) -> int:
+    block, block_bytes = QUANT_LAYOUT[qtype]
+    ncols = int(row["shape"][-1])
+    if ncols % block:
+        raise ValueError(
+            f"{row['name']}: row width {ncols} is not divisible by {qtype} block {block}"
+        )
+    return math.prod(int(value) for value in row["shape"][:-1]) * (ncols // block) * block_bytes
+
+
+def part_number(row: dict[str, Any]) -> int:
+    match = re.search(r"part_(\d+)\.weight$", str(row["name"]))
+    if match is None:
+        raise ValueError(f"cannot derive PLE logical part from {row['name']}")
+    return int(match.group(1))
+
+
+def target_type(row: dict[str, Any]) -> str:
+    role = row["role"]
+    if role == "ngram":
+        raise ValueError("PLE table is external and has no main-GGUF target type")
+    if role == "routed_gate_up_main":
+        return "Q5_K" if int(row["layer"]) in {0, 1, 46, 47} else "Q4_K"
+    if role == "routed_down_main":
+        return "Q5_K" if row["physical_part"] == "main" else "Q5_0"
+    return str(row["target_types"][BASELINE_VARIANT])
+
+
+def canonical_region(row: dict[str, Any]) -> str:
+    role = row["role"]
+    if role == "ngram":
+        return CANONICAL_REGIONS[0]
+    if role == "routed_gate_up_main":
+        return CANONICAL_REGIONS[2] if int(row["layer"]) in {0, 1, 46, 47} else CANONICAL_REGIONS[1]
+    if role == "routed_down_main":
+        return CANONICAL_REGIONS[3] if row["physical_part"] == "main" else CANONICAL_REGIONS[4]
+    if role == "integer_control":
+        return CANONICAL_REGIONS[8]
+    if role in {"hc_projection", "conv", "unaligned_matrix"}:
+        return CANONICAL_REGIONS[6]
+    if role in {"norm", "router", "control", "state", "bias"}:
+        return CANONICAL_REGIONS[7]
+    return CANONICAL_REGIONS[5]
+
+
+def load_tensor(model_dir: Path, weight_map: dict[str, str], name: str) -> list[int]:
+    path = model_dir / weight_map[name]
+    with safe_open(str(path), framework="pt", device="cpu") as handle:
+        return [int(value) for value in handle.get_tensor(name).reshape(-1).tolist()]
+
+
+def main() -> int:
+    args = parse_args()
+    if args.physical_files <= 0 or EXPECTED_PLE_PARTS % args.physical_files:
+        raise SystemExit("--physical-files must be a positive divisor of 128")
+    source_map = json.loads(args.source_map.read_text())
+    if source_map.get("source") != {"repo": SOURCE_REPO, "revision": SOURCE_REVISION}:
+        raise SystemExit(f"unexpected source pin: {source_map.get('source')}")
+    rows: list[dict[str, Any]] = source_map["tensors"]
+    if sum(parameters(row) for row in rows) != EXPECTED_TOTAL_PARAMETERS:
+        raise SystemExit("source map parameter total changed")
+
+    ple_rows = sorted((row for row in rows if row["role"] == "ngram"), key=part_number)
+    main_rows = [row for row in rows if row["role"] != "ngram"]
+    if len(ple_rows) != EXPECTED_PLE_PARTS or [part_number(row) for row in ple_rows] != list(range(EXPECTED_PLE_PARTS)):
+        raise SystemExit("PLE source map is not the exact 128-part logical sequence")
+    if any(row["shape"] != [EXPECTED_ROWS_PER_PART, EXPECTED_ROW_WIDTH] for row in ple_rows):
+        raise SystemExit("PLE part shape changed")
+    if sum(parameters(row) for row in main_rows) != EXPECTED_MAIN_PARAMETERS:
+        raise SystemExit("main-backbone parameter total changed")
+
+    main_map_rows: list[dict[str, Any]] = []
+    for original in main_rows:
+        row = dict(original)
+        qtype = target_type(row)
+        row["canonical_region"] = canonical_region(row)
+        row["target_types"] = {VARIANT: qtype}
+        row["bytes_by_variant"] = {VARIANT: storage_bytes(row, qtype)}
+        main_map_rows.append(row)
+
+    index = json.loads((args.model_dir / "model.safetensors.index.json").read_text())
+    weight_map: dict[str, str] = index["weight_map"]
+    prefix = "model.language_model.layers.1.ple.ple_embedding."
+    head_vocab_sizes = load_tensor(args.model_dir, weight_map, prefix + "ngram_heads_vocab_sizes")
+    head_offsets = load_tensor(args.model_dir, weight_map, prefix + "ngram_heads_offsets")
+    layer_multipliers = load_tensor(args.model_dir, weight_map, prefix + "layer_multipliers")
+    usable_vocab_rows = sum(head_vocab_sizes)
+    padded_vocab_rows = EXPECTED_PLE_PARTS * EXPECTED_ROWS_PER_PART
+    if align(usable_vocab_rows, 128) != padded_vocab_rows:
+        raise SystemExit("PLE head vocab sizes do not match the 128-row padded embedding size")
+
+    parts_per_file = EXPECTED_PLE_PARTS // args.physical_files
+    physical_files = [
+        {
+            "index": index,
+            "path": f"ple/ple-bf16-{index + 1:05d}-of-{args.physical_files:05d}.bin",
+            "payload_bytes": 0,
+            "file_bytes": 0,
+            "sha256": None,
+        }
+        for index in range(args.physical_files)
+    ]
+    logical_parts = []
+    for part, row in enumerate(ple_rows):
+        file_index = part // parts_per_file
+        file_entry = physical_files[file_index]
+        offset = align(int(file_entry["file_bytes"]))
+        payload_bytes = parameters(row) * 2
+        logical_parts.append(
+            {
+                "logical_part": part,
+                "global_row_start": part * EXPECTED_ROWS_PER_PART,
+                "rows": EXPECTED_ROWS_PER_PART,
+                "row_stride_bytes": EXPECTED_ROW_WIDTH * 2,
+                "embedding_row_dimension": EXPECTED_ROW_WIDTH,
+                "source_name": row["source_name"],
+                "source_shard": row["source_shard"],
+                "physical_file_index": file_index,
+                "physical_file": file_entry["path"],
+                "file_offset": offset,
+                "payload_bytes": payload_bytes,
+                "sha256": None,
+            }
+        )
+        file_entry["payload_bytes"] = int(file_entry["payload_bytes"]) + payload_bytes
+        file_entry["file_bytes"] = offset + payload_bytes
+    for file_entry in physical_files:
+        file_entry["file_bytes"] = align(int(file_entry["file_bytes"]))
+
+    sidecar_payload_bytes = sum(int(item["payload_bytes"]) for item in logical_parts)
+    sidecar_file_bytes = sum(int(item["file_bytes"]) for item in physical_files)
+    ple_manifest = {
+        "format_version": 1,
+        "artifact_variant": VARIANT,
+        "source": {"repo": SOURCE_REPO, "revision": SOURCE_REVISION},
+        "storage": "ssd_backed_bounded_page_cache",
+        "storage_dtype": "BF16",
+        "byte_order": "little",
+        "number_of_ple_layers": 1,
+        "ple_checkpoint_layer_zero_based": 1,
+        "number_of_ngram_heads": len(head_vocab_sizes),
+        "ngram_size": 3,
+        "heads_per_ngram": 8,
+        "embedding_row_dimension": EXPECTED_ROW_WIDTH,
+        "ple_embedding_dimension": EXPECTED_ROW_WIDTH * len(head_vocab_sizes),
+        "per_head_vocabulary_sizes": head_vocab_sizes,
+        "per_head_offsets": head_offsets,
+        "usable_vocabulary_rows": usable_vocab_rows,
+        "padded_vocabulary_rows": padded_vocab_rows,
+        "padding_rows": padded_vocab_rows - usable_vocab_rows,
+        "layer_multipliers": layer_multipliers,
+        "logical_shard_count": EXPECTED_PLE_PARTS,
+        "physical_file_count": args.physical_files,
+        "alignment_bytes": ALIGNMENT,
+        "row_stride_bytes": EXPECTED_ROW_WIDTH * 2,
+        "total_parameter_count": EXPECTED_PLE_PARTS * EXPECTED_ROWS_PER_PART * EXPECTED_ROW_WIDTH,
+        "total_payload_bytes": sidecar_payload_bytes,
+        "total_file_bytes_including_alignment": sidecar_file_bytes,
+        "logical_parts": logical_parts,
+        "physical_files": physical_files,
+        "checksum_status": "pending_extraction",
+        "hash_reference": {
+            "implementation": "transformers.models.qwen4_exp.modeling_qwen4_exp.Qwen4ExpTextNGramEmbedding",
+            "transformers_version": "5.16.0",
+            "implementation_sha256": "77fec77d87f2a0eb23b95fa04276fb5779698a7c7f523cf5061e49c118bcc459",
+            "semantics": "SplitMix64-derived odd multipliers; multiply token ids; XOR in n-gram position order; modulo per-head prime vocabulary; add per-head offset; EOS-bounded history",
+        },
+    }
+
+    baseline_payload = sum(int(row["bytes_by_variant"][BASELINE_VARIANT]) for row in rows)
+    baseline_ple_bytes = sum(int(row["bytes_by_variant"][BASELINE_VARIANT]) for row in ple_rows)
+    main_payload = sum(int(row["bytes_by_variant"][VARIANT]) for row in main_map_rows)
+    regions = []
+    for name in CANONICAL_REGIONS:
+        selected = [row for row in rows if canonical_region(row) == name]
+        old_bytes = sum(int(row["bytes_by_variant"][BASELINE_VARIANT]) for row in selected)
+        if name == CANONICAL_REGIONS[0]:
+            new_resident_bytes = 0
+            new_storage_bytes = sidecar_payload_bytes
+            new_type = "BF16 SSD sidecar"
+        else:
+            selected_names = {row["name"] for row in selected}
+            planned = [row for row in main_map_rows if row["name"] in selected_names]
+            new_resident_bytes = sum(int(row["bytes_by_variant"][VARIANT]) for row in planned)
+            new_storage_bytes = new_resident_bytes
+            new_type = "+".join(sorted({row["target_types"][VARIANT] for row in planned}))
+        regions.append(
+            {
+                "canonical_name": name,
+                "tensor_count": len(selected),
+                "parameters": sum(parameters(row) for row in selected),
+                "existing_resident_bytes": old_bytes,
+                "new_resident_bytes": new_resident_bytes,
+                "new_storage_bytes": new_storage_bytes,
+                "resident_delta_bytes": new_resident_bytes - old_bytes,
+                "new_type": new_type,
+            }
+        )
+
+    projection = {
+        "schema_version": 1,
+        "variant": VARIANT,
+        "source": {"repo": SOURCE_REPO, "revision": SOURCE_REVISION},
+        "baseline_variant": BASELINE_VARIANT,
+        "baseline_resident_payload_bytes": baseline_payload,
+        "baseline_ple_q5_1_resident_bytes": baseline_ple_bytes,
+        "main_backbone_parameters": EXPECTED_MAIN_PARAMETERS,
+        "main_gguf_tensor_count": len(main_map_rows),
+        "main_resident_payload_bytes": main_payload,
+        "resident_backbone_bpw": main_payload * 8 / EXPECTED_MAIN_PARAMETERS,
+        "resident_delta_vs_baseline_bytes": main_payload - baseline_payload,
+        "resident_delta_vs_baseline_gib": (main_payload - baseline_payload) / 2**30,
+        "compute_backbone_uplift_bytes": main_payload - (baseline_payload - baseline_ple_bytes),
+        "compute_backbone_uplift_gib": (main_payload - (baseline_payload - baseline_ple_bytes)) / 2**30,
+        "ple_storage_dtype": "BF16",
+        "ple_storage_bpw": 16.0,
+        "ple_sidecar_payload_bytes": sidecar_payload_bytes,
+        "ple_sidecar_file_bytes_including_alignment": sidecar_file_bytes,
+        "total_artifact_payload_bytes": main_payload + sidecar_payload_bytes,
+        "regions": regions,
+        "tail_format_decision": {
+            "requested": "Q5_K",
+            "realized": "Q5_0",
+            "reason": "standard GGUF Q5_K requires ne[0] divisible by 256; the semantic tail has ne[0]=128",
+            "effective_bpw": 5.5,
+            "paired_row_q5_k_status": "deferred_custom_runtime_experiment",
+        },
+        "acceptance_note": "Payload census excludes GGUF metadata/alignment and runtime allocations; final residency must be measured on DGX Spark.",
+    }
+
+    type_counts = collections.Counter(row["target_types"][VARIANT] for row in main_map_rows)
+    type_bytes = collections.Counter()
+    for row in main_map_rows:
+        type_bytes[row["target_types"][VARIANT]] += int(row["bytes_by_variant"][VARIANT])
+    expected_counts = {
+        "BF16": 363,
+        "F32": 584,
+        "I64": 3,
+        "Q4_K": 88,
+        "Q5_0": 48,
+        "Q5_K": 56,
+        "Q8_0": 486,
+    }
+    expected_bytes = {
+        "BF16": 1_593_061_376,
+        "F32": 263_372_224,
+        "I64": 280,
+        "Q4_K": 41_523_609_600,
+        "Q5_0": 5_536_481_280,
+        "Q5_K": 26_759_659_520,
+        "Q8_0": 7_587_744_640,
+    }
+    if dict(sorted(type_counts.items())) != expected_counts:
+        raise SystemExit(f"Q5 recipe type-count mismatch: {dict(sorted(type_counts.items()))}")
+    if dict(sorted(type_bytes.items())) != expected_bytes:
+        raise SystemExit(f"Q5 recipe byte-count mismatch: {dict(sorted(type_bytes.items()))}")
+    if sum(type_bytes.values()) != 83_263_928_920:
+        raise SystemExit(f"Q5 recipe payload mismatch: {sum(type_bytes.values())}")
+    main_map = {
+        "schema_version": 2,
+        "source": {"repo": SOURCE_REPO, "revision": SOURCE_REVISION},
+        "source_map_parent": str(args.source_map),
+        "source_map_parent_sha256": sha256(args.source_map),
+        "source_tensor_count": len({row["source_name"] for row in main_map_rows}),
+        "gguf_tensor_count": len(main_map_rows),
+        "logical_parameters": EXPECTED_MAIN_PARAMETERS,
+        "variants": [VARIANT],
+        "canonical_regions": list(CANONICAL_REGIONS),
+        "target_type_counts": dict(sorted(type_counts.items())),
+        "target_type_bytes": dict(sorted(type_bytes.items())),
+        "external_artifacts": {
+            "ple": {
+                "storage": "ssd_backed_bounded_page_cache",
+                "dtype": "BF16",
+                "resident": False,
+                "manifest": "ple/ple-manifest.json",
+                "payload_bytes": sidecar_payload_bytes,
+                "file_bytes_including_alignment": sidecar_file_bytes,
+                "alignment": ALIGNMENT,
+            }
+        },
+        "tensors": main_map_rows,
+    }
+    extraction_map = {
+        "schema_version": 1,
+        "variant": VARIANT,
+        "source": {"repo": SOURCE_REPO, "revision": SOURCE_REVISION},
+        "source_map_parent": str(args.source_map),
+        "source_map_parent_sha256": sha256(args.source_map),
+        "storage_dtype": "BF16",
+        "alignment_bytes": ALIGNMENT,
+        "logical_parts": logical_parts,
+        "physical_files": physical_files,
+        "total_payload_bytes": sidecar_payload_bytes,
+        "total_file_bytes_including_alignment": sidecar_file_bytes,
+    }
+
+    args.out_dir.mkdir(parents=True, exist_ok=True)
+    outputs = {
+        "main-gguf-source-map.json": main_map,
+        "ple-extraction-map.json": extraction_map,
+        "ple-manifest.plan.json": ple_manifest,
+        "projection-ssd-ple.json": projection,
+    }
+    for name, payload in outputs.items():
+        path = args.out_dir / name
+        path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+        print(f"wrote {path}")
+    print(f"main payload: {main_payload:,} bytes ({main_payload / 2**30:.6f} GiB)")
+    print(f"BF16 PLE payload: {sidecar_payload_bytes:,} bytes ({sidecar_payload_bytes / 2**30:.6f} GiB)")
+    print(f"resident delta: {main_payload - baseline_payload:,} bytes ({(main_payload - baseline_payload) / 2**30:.6f} GiB)")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

reproduction/converter/convert_qwen4exp_gguf.py

@@ -0,0 +1,821 @@
+#!/usr/bin/env python3
+"""Convert the pinned Qwen3.8-Flash-Next checkpoint to audited GGUF shards.
+
+BF16 is a bit-exact physical reshape of the source BF16 payload. Q8_0 is the
+public calibration/reference artifact. The audited ds4 C block writers provide
+Q2_K/Q4_K/Q5_K/Q6_K and imatrix-weighted IQ2_XXS payloads; unsupported quant types are
+never silently approximated by this Python converter.
+"""
+
+from __future__ import annotations
+
+import argparse
+import ctypes
+import hashlib
+import json
+import os
+import struct
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from safetensors import safe_open
+
+
+SOURCE_REPO = "ukisai/Swift1.5-Qwen3.8-Flash-Next"
+SOURCE_REVISION = "0bd4fe22431372cdad1979267d3ab45aa7e6150a"
+MODEL_BASENAME = "Swift1.5-Qwen3.8-Flash-Next"
+SOURCE_LICENSE = "other"
+SOURCE_LICENSE_NAME = "swift-open-license-1.0"
+SOURCE_LICENSE_URL = (
+    f"https://huggingface.co/{SOURCE_REPO}/blob/{SOURCE_REVISION}/LICENSE"
+)
+SOURCE_LICENSE_SHA256 = "7a3af3e6b43fcb7c486ea8167252a36a9532dcbdf02d1704a749881872ec551c"
+ARCH = "qwen4exp"
+ALIGNMENT = 32
+SUPPORTED_PAYLOAD_TYPES = {
+    "BF16",
+    "F32",
+    "I64",
+    "Q8_0",
+    "Q4_0",
+    "Q5_0",
+    "Q5_1",
+    "Q2_K",
+    "Q4_K",
+    "Q5_K",
+    "Q6_K",
+    "IQ2_XXS",
+}
+DS4_QUANT_TYPES = {
+    "Q2_K": 10,
+    "Q4_K": 12,
+    "Q5_K": 13,
+    "Q6_K": 14,
+    "IQ2_XXS": 16,
+}
+IMATRIX_REQUIRED_TYPES = {"IQ2_XXS"}
+_DS4_QUANTS: ctypes.CDLL | None = None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-dir", type=Path, required=True)
+    parser.add_argument("--source-map", type=Path, required=True)
+    parser.add_argument("--variant", required=True)
+    parser.add_argument(
+        "--imatrix",
+        type=Path,
+        help="Official-model legacy imatrix required by IQ2_XXS payloads.",
+    )
+    parser.add_argument("--out", type=Path, required=True)
+    parser.add_argument("--artifact-manifest", type=Path, required=True)
+    parser.add_argument(
+        "--template-only",
+        action="store_true",
+        help="Write GGUF metadata/tensor directory only for the C quantizer.",
+    )
+    parser.add_argument(
+        "--split-max-size-gib",
+        type=float,
+        default=30.0,
+        help="Direct-output shard ceiling; ignored by --template-only (default: 30).",
+    )
+    parser.add_argument("--hash-output", action="store_true")
+    parser.add_argument(
+        "--sha256sums",
+        type=Path,
+        help="Write a coreutils-compatible checksum file (requires --hash-output).",
+    )
+    return parser.parse_args()
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(16 << 20), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def load_json(path: Path) -> dict[str, Any]:
+    return json.loads(path.read_text())
+
+
+def _read_exact(handle: Any, size: int, label: str) -> bytes:
+    payload = handle.read(size)
+    if len(payload) != size:
+        raise RuntimeError(f"short imatrix read for {label}: {len(payload)} != {size}")
+    return payload
+
+
+def _read_i32(handle: Any, label: str) -> int:
+    return int(struct.unpack("<i", _read_exact(handle, 4, label))[0])
+
+
+def load_imatrix(path: Path) -> tuple[dict[str, np.ndarray], dict[str, Any]]:
+    """Load the legacy llama.cpp imatrix format with strict bounds checks."""
+    if not path.is_file():
+        raise FileNotFoundError(path)
+    entries: dict[str, np.ndarray] = {}
+    with path.open("rb") as handle:
+        entry_count = _read_i32(handle, "entry count")
+        if not 1 <= entry_count <= 100_000:
+            raise RuntimeError(f"invalid imatrix entry count {entry_count}")
+        for index in range(entry_count):
+            name_length = _read_i32(handle, f"entry {index} name length")
+            if not 1 <= name_length <= 4096:
+                raise RuntimeError(
+                    f"entry {index}: invalid imatrix name length {name_length}"
+                )
+            raw_name = _read_exact(handle, name_length, f"entry {index} name")
+            try:
+                name = raw_name.decode("utf-8")
+            except UnicodeDecodeError as error:
+                raise RuntimeError(f"entry {index}: imatrix name is not UTF-8") from error
+            calls = _read_i32(handle, f"entry {index} calls")
+            value_count = _read_i32(handle, f"entry {index} value count")
+            if calls < 0 or not 1 <= value_count <= 100_000_000:
+                raise RuntimeError(
+                    f"entry {index}: invalid calls/value count {calls}/{value_count}"
+                )
+            raw_values = _read_exact(
+                handle, value_count * 4, f"entry {index} values"
+            )
+            values = np.frombuffer(raw_values, dtype="<f4").copy()
+            if not np.isfinite(values).all() or (values < 0).any():
+                raise RuntimeError(f"{name}: imatrix values must be finite and non-negative")
+            if name in entries:
+                raise RuntimeError(f"duplicate imatrix entry {name!r}")
+            entries[name] = values
+
+        trailer = handle.read(4)
+        chunks: int | None = None
+        dataset: str | None = None
+        if trailer:
+            if len(trailer) != 4:
+                raise RuntimeError("truncated imatrix chunks trailer")
+            chunks = int(struct.unpack("<i", trailer)[0])
+            dataset_length = _read_i32(handle, "dataset length")
+            if chunks < 0 or not 0 <= dataset_length <= 1_000_000:
+                raise RuntimeError(
+                    f"invalid imatrix trailer chunks/dataset length {chunks}/{dataset_length}"
+                )
+            raw_dataset = _read_exact(handle, dataset_length, "dataset")
+            try:
+                dataset = raw_dataset.decode("utf-8")
+            except UnicodeDecodeError as error:
+                raise RuntimeError("imatrix dataset is not UTF-8") from error
+        if handle.read(1):
+            raise RuntimeError("unexpected trailing bytes in imatrix")
+
+    metadata: dict[str, Any] = {
+        "path": str(path),
+        "bytes": path.stat().st_size,
+        "sha256": sha256_file(path),
+        "entries_count": len(entries),
+        "chunks_count": chunks,
+        "dataset": dataset,
+    }
+    return entries, metadata
+
+
+def imatrix_for_tensor(
+    row: dict[str, Any], entries: dict[str, np.ndarray]
+) -> np.ndarray:
+    """Return a contiguous [expert, input-column] matrix for a routed tensor."""
+    name = str(row["name"])
+    if name not in entries:
+        raise RuntimeError(f"missing required imatrix entry {name!r}")
+    shape = tuple(int(value) for value in row["shape"])
+    if len(shape) != 3:
+        raise RuntimeError(f"{name}: IQ2_XXS requires a 3-D routed expert tensor")
+    expert_count, _, ncols = shape
+    values = entries[name]
+    expected = expert_count * ncols
+    if values.size != expected:
+        raise RuntimeError(
+            f"{name}: imatrix has {values.size} values, expected {expected}"
+        )
+    return np.ascontiguousarray(values.reshape(expert_count, ncols), dtype=np.float32)
+
+
+def ensure_source_pin(source_map: dict[str, Any]) -> None:
+    source = source_map.get("source", {})
+    if source.get("repo") != SOURCE_REPO or source.get("revision") != SOURCE_REVISION:
+        raise SystemExit(f"source-map pin mismatch: {source!r}")
+
+
+def ensure_source_license(model_dir: Path) -> None:
+    license_path = model_dir / "LICENSE"
+    if not license_path.is_file():
+        raise SystemExit(f"missing pinned source license: {license_path}")
+    digest = sha256_file(license_path)
+    if digest != SOURCE_LICENSE_SHA256:
+        raise SystemExit(
+            f"source LICENSE digest mismatch: {digest} != {SOURCE_LICENSE_SHA256}"
+        )
+
+
+def variant_label(variant: str) -> tuple[str, str, int]:
+    from gguf import LlamaFileType
+
+    if variant == "BF16":
+        return (
+            "Qwen3.8-Flash-Next BF16",
+            "BF16-LOSSLESS-SEMANTIC-SPLIT",
+            int(LlamaFileType.MOSTLY_BF16),
+        )
+    if variant == "Q8_0":
+        return (
+            "Qwen3.8-Flash-Next Q8_0",
+            "Q8_0-REFERENCE",
+            int(LlamaFileType.MOSTLY_Q8_0),
+        )
+    if variant == "MQ-NG5-Q2K-Q4-SPLIT-E4":
+        return (
+            f"Qwen3.8-Flash-Next {variant}",
+            variant,
+            int(LlamaFileType.MOSTLY_Q2_K),
+        )
+    if variant == "MQ-Q6-SSD-PLE-BF16":
+        return (
+            "Qwen3.8-Flash-Next MQ-Q6-SSD-PLE-BF16",
+            variant,
+            int(LlamaFileType.MOSTLY_Q5_K_M),
+        )
+    if variant == "MQ-Q5-SSD-PLE-BF16":
+        return (
+            f"{MODEL_BASENAME} MQ-Q5-SSD-PLE-BF16",
+            variant,
+            int(LlamaFileType.MOSTLY_Q4_K_M),
+        )
+    return (
+        f"Qwen3.8-Flash-Next {variant}",
+        variant,
+        int(LlamaFileType.MOSTLY_IQ2_XXS),
+    )
+
+
+def add_metadata(writer: Any, model_dir: Path, source_map_path: Path, variant: str) -> None:
+    config = load_json(model_dir / "config.json")
+    text = config["text_config"]
+    vision = config["vision_config"]
+    generation = load_json(model_dir / "generation_config.json")
+    name, quantization, file_type = variant_label(variant)
+
+    writer.add_name(name)
+    writer.add_description("Independent Baekpica mixed-quant conversion of Swift1.5; "
+                           "weights converted from source BF16. See NOTICE for changes.")
+    writer.add_type("model")
+    writer.add_basename(MODEL_BASENAME)
+    writer.add_size_label("180B-A6B")
+    # Preserve Swift licensing; LICENSE-QWEN also governs the base weights.
+    writer.add_license(SOURCE_LICENSE)
+    writer.add_license_name(SOURCE_LICENSE_NAME)
+    writer.add_license_link(SOURCE_LICENSE_URL)
+    writer.add_repo_url(f"https://huggingface.co/{SOURCE_REPO}")
+    writer.add_source_repo_url(f"https://huggingface.co/{SOURCE_REPO}")
+    writer.add_string("general.source.revision", SOURCE_REVISION)
+    writer.add_string("general.quantization", quantization)
+    writer.add_quantization_version(2)
+    writer.add_file_type(file_type)
+
+    writer.add_block_count(int(text["num_hidden_layers"]))
+    writer.add_context_length(int(text["max_position_embeddings"]))
+    writer.add_embedding_length(int(text["hidden_size"]))
+    writer.add_vocab_size(int(text["vocab_size"]))
+    writer.add_feed_forward_length(int(text["moe_intermediate_size"]))
+    writer.add_head_count(int(text["num_attention_heads"]))
+    writer.add_head_count_kv(int(text["num_key_value_heads"]))
+    writer.add_key_length(int(text["head_dim"]))
+    writer.add_value_length(int(text["head_dim"]))
+    writer.add_layer_norm_rms_eps(float(text["rms_norm_eps"]))
+    writer.add_rope_freq_base(float(text["rope_parameters"]["rope_theta"]))
+    writer.add_rope_dimension_count(int(text["head_dim"] * text["partial_rotary_factor"]))
+    writer.add_rope_dimension_sections(list(text["rope_parameters"]["mrope_section"]))
+    writer.add_expert_count(int(text["num_experts"]))
+    writer.add_expert_used_count(int(text["num_experts_per_tok"]))
+    writer.add_expert_feed_forward_length(int(text["moe_intermediate_size"]))
+    writer.add_expert_shared_count(1)
+    writer.add_expert_shared_feed_forward_length(int(text["shared_expert_intermediate_size"]))
+    writer.add_nextn_predict_layers(int(text["mtp_num_hidden_layers"]))
+    writer.add_full_attention_interval(int(text["full_attention_interval"]))
+    writer.add_indexer_head_count(int(text["indexer_n_heads"]))
+    writer.add_indexer_key_length(int(text["indexer_head_dim"]))
+    writer.add_indexer_top_k(int(text["indexer_budget"]))
+
+    custom: dict[str, Any] = {
+        f"{ARCH}.attention.layer_types": list(text["layer_types"]),
+        f"{ARCH}.attention.full_layers": [i for i, kind in enumerate(text["layer_types"]) if kind == "full_attention"],
+        f"{ARCH}.attention.linear_key_head_count": int(text["linear_num_key_heads"]),
+        f"{ARCH}.attention.linear_value_head_count": int(text["linear_num_value_heads"]),
+        f"{ARCH}.attention.linear_key_length": int(text["linear_key_head_dim"]),
+        f"{ARCH}.attention.linear_value_length": int(text["linear_value_head_dim"]),
+        f"{ARCH}.attention.linear_conv_kernel": int(text["linear_conv_kernel_dim"]),
+        f"{ARCH}.attention.output_gate_type": str(text["output_gate_type"]),
+        f"{ARCH}.attention.indexer.compress_ratio": int(text["indexer_compress_ratio"]),
+        f"{ARCH}.attention.indexer.kv_head_count": int(text["indexer_kv_heads"]),
+        f"{ARCH}.hyper_connection.count": int(text["hc_count"]),
+        f"{ARCH}.hyper_connection.lowrank": int(text["hc_lowrank"]),
+        f"{ARCH}.rms_norm.zero_centered": True,
+        f"{ARCH}.ple.layer_ids_one_based": list(text["ple_layer_ids"]),
+        f"{ARCH}.ple.checkpoint_layer_ids_zero_based": [int(x) - 1 for x in text["ple_layer_ids"]],
+        f"{ARCH}.ple.ngram_size": int(text["ngram_size"]),
+        f"{ARCH}.ple.heads_per_ngram": int(text["heads_per_ngram"]),
+        f"{ARCH}.ple.ngram_vocab_size_base": int(text["ngram_vocab_size_base"]),
+        f"{ARCH}.ple.ngram_parts": int(text["split_ngram_parts"]),
+        f"{ARCH}.ple.embedding_length": int(text["ple_embed_dim"]),
+        f"{ARCH}.ple.conv_kernel": int(text["ple_conv_kernel_size"]),
+        f"{ARCH}.mtp.present": True,
+        f"{ARCH}.mtp.block_count": int(text["mtp"]["num_hidden_layers"]),
+        f"{ARCH}.vision.present": True,
+        f"{ARCH}.vision.block_count": int(vision["depth"]),
+        f"{ARCH}.vision.embedding_length": int(vision["hidden_size"]),
+        f"{ARCH}.vision.feed_forward_length": int(vision["intermediate_size"]),
+        f"{ARCH}.vision.head_count": int(vision["num_heads"]),
+        f"{ARCH}.vision.output_embedding_length": int(vision["out_hidden_size"]),
+        f"{ARCH}.vision.patch_size": int(vision["patch_size"]),
+        f"{ARCH}.vision.temporal_patch_size": int(vision["temporal_patch_size"]),
+        f"{ARCH}.vision.spatial_merge_size": int(vision["spatial_merge_size"]),
+        f"{ARCH}.vision.position_count": int(vision["num_position_embeddings"]),
+        f"{ARCH}.tokenizer.eog_token_ids": list(generation["eos_token_id"]),
+        f"{ARCH}.source.config_sha256": sha256_file(model_dir / "config.json"),
+        f"{ARCH}.source.index_sha256": sha256_file(model_dir / "model.safetensors.index.json"),
+        f"{ARCH}.source.tokenizer_sha256": sha256_file(model_dir / "tokenizer.json"),
+        f"{ARCH}.source.chat_template_sha256": sha256_file(model_dir / "chat_template.jinja"),
+        f"{ARCH}.source.license_sha256": sha256_file(model_dir / "LICENSE"),
+        f"{ARCH}.source.gguf_map_sha256": sha256_file(source_map_path),
+    }
+    source_map = load_json(source_map_path)
+    ple_external = source_map.get("external_artifacts", {}).get("ple")
+    if ple_external:
+        custom.update(
+            {
+                f"{ARCH}.ple.weight_storage": str(ple_external["storage"]),
+                f"{ARCH}.ple.storage_dtype": str(ple_external["dtype"]),
+                f"{ARCH}.ple.resident_weight": False,
+                f"{ARCH}.ple.sidecar_manifest": str(ple_external["manifest"]),
+                f"{ARCH}.ple.sidecar_payload_bytes": int(ple_external["payload_bytes"]),
+                f"{ARCH}.ple.sidecar_alignment": int(ple_external["alignment"]),
+            }
+        )
+    for key, value in custom.items():
+        if isinstance(value, bool):
+            writer.add_bool(key, value)
+        elif isinstance(value, int):
+            writer.add_uint64(key, value) if value > 0xFFFFFFFF else writer.add_uint32(key, value)
+        elif isinstance(value, float):
+            writer.add_float32(key, value)
+        elif isinstance(value, str):
+            writer.add_string(key, value)
+        elif isinstance(value, list):
+            writer.add_array(key, value)
+        else:
+            raise TypeError(f"unsupported metadata value {key}: {type(value)}")
+
+
+def add_imatrix_metadata(writer: Any, metadata: dict[str, Any]) -> None:
+    """Embed the same provenance keys consumed by the ds4 C quantizer."""
+    writer.add_string("quantize.imatrix.file", Path(metadata["path"]).name)
+    writer.add_uint64("quantize.imatrix.entries_count", int(metadata["entries_count"]))
+    if metadata.get("dataset"):
+        writer.add_string("quantize.imatrix.dataset", str(metadata["dataset"]))
+    if metadata.get("chunks_count") is not None:
+        writer.add_uint64("quantize.imatrix.chunks_count", int(metadata["chunks_count"]))
+    writer.add_string("quantize.imatrix.sha256", str(metadata["sha256"]))
+
+
+def add_tokenizer(writer: Any, model_dir: Path) -> None:
+    from gguf import TokenType
+    from transformers import AutoTokenizer
+
+    config = load_json(model_dir / "config.json")
+    generation = load_json(model_dir / "generation_config.json")
+    tokenizer_json = load_json(model_dir / "tokenizer.json")
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, local_files_only=True)
+    vocab_size = int(config["text_config"]["vocab_size"])
+    reverse = {token_id: token for token, token_id in tokenizer.get_vocab().items()}
+    added = {int(row["id"]): row for row in tokenizer_json.get("added_tokens", [])}
+    tokens: list[str] = []
+    token_types: list[int] = []
+    for token_id in range(vocab_size):
+        token = reverse.get(token_id)
+        if token is None:
+            tokens.append(f"[UNUSED_{token_id}]")
+            token_types.append(TokenType.UNUSED)
+        else:
+            tokens.append(token)
+            if token_id in added:
+                token_types.append(
+                    TokenType.CONTROL if added[token_id].get("special") else TokenType.USER_DEFINED
+                )
+            else:
+                token_types.append(TokenType.NORMAL)
+    merges = tokenizer_json["model"]["merges"]
+    if merges and not isinstance(merges[0], str):
+        merges = [" ".join(pair) for pair in merges]
+    writer.add_tokenizer_model("gpt2")
+    writer.add_tokenizer_pre("qwen2")
+    writer.add_token_list(tokens)
+    writer.add_token_types(token_types)
+    writer.add_token_merges(merges)
+    writer.add_bos_token_id(int(generation["bos_token_id"]))
+    writer.add_eos_token_id(int(generation["eos_token_id"][0]))
+    writer.add_pad_token_id(int(generation["pad_token_id"]))
+    writer.add_add_bos_token(False)
+    writer.add_add_eos_token(False)
+    writer.add_chat_template((model_dir / "chat_template.jinja").read_text())
+
+
+class ShardStore:
+    """One-tensor cache, enough to reuse the consecutive gate/up and down slices."""
+
+    def __init__(self, model_dir: Path, weight_map: dict[str, str]):
+        self.model_dir = model_dir
+        self.weight_map = weight_map
+        self.cached_name: str | None = None
+        self.cached_tensor: torch.Tensor | None = None
+
+    def load(self, name: str) -> torch.Tensor:
+        if name == self.cached_name and self.cached_tensor is not None:
+            return self.cached_tensor
+        shard = self.weight_map[name]
+        path = self.model_dir / shard
+        if not path.is_file():
+            raise FileNotFoundError(path)
+        with safe_open(str(path), framework="pt", device="cpu") as handle:
+            tensor = handle.get_tensor(name)
+        self.cached_name = name
+        self.cached_tensor = tensor
+        return tensor
+
+    def clear(self) -> None:
+        self.cached_name = None
+        self.cached_tensor = None
+
+
+def materialize_source(row: dict[str, Any], store: ShardStore) -> torch.Tensor:
+    tensor = store.load(row["source_name"])
+    if list(tensor.shape) != row["source_shape"]:
+        raise ValueError(
+            f"{row['source_name']}: loaded {list(tensor.shape)} != {row['source_shape']}"
+        )
+    source_slice = row.get("source_slice")
+    if source_slice is not None:
+        slices: list[slice] = [slice(None)] * tensor.ndim
+        axis = int(source_slice["axis"])
+        slices[axis] = slice(int(source_slice["start"]), int(source_slice["stop"]))
+        tensor = tensor[tuple(slices)]
+    tensor = tensor.contiguous()
+    if list(tensor.shape) != row["shape"]:
+        raise ValueError(f"{row['name']}: sliced {list(tensor.shape)} != {row['shape']}")
+    return tensor
+
+
+def load_ds4_quants() -> ctypes.CDLL:
+    global _DS4_QUANTS
+    if _DS4_QUANTS is not None:
+        return _DS4_QUANTS
+    default = Path(__file__).resolve().parents[2] / "ds4" / "gguf-tools" / "libds4quants.so"
+    path = Path(os.environ.get("DS4_QUANTS_LIBRARY", default))
+    if not path.is_file():
+        raise RuntimeError(
+            f"missing ds4 quantization library {path}; run `make -C ds4/gguf-tools libds4quants.so`"
+        )
+    library = ctypes.CDLL(str(path))
+    library.ds4q_row_size.argtypes = [ctypes.c_int, ctypes.c_int64]
+    library.ds4q_row_size.restype = ctypes.c_size_t
+    library.ds4q_quantize_chunk.argtypes = [
+        ctypes.c_int,
+        ctypes.c_void_p,
+        ctypes.c_void_p,
+        ctypes.c_int64,
+        ctypes.c_int64,
+        ctypes.c_int64,
+        ctypes.c_void_p,
+    ]
+    library.ds4q_quantize_chunk.restype = ctypes.c_size_t
+    library.ds4q_quantize_init.argtypes = [ctypes.c_int]
+    library.ds4q_quantize_init.restype = None
+    _DS4_QUANTS = library
+    return library
+
+
+def quantize_ds4(
+    f32: np.ndarray,
+    qtype_name: str,
+    imatrix: np.ndarray | None = None,
+) -> np.ndarray:
+    """Quantize with ds4's C block writer, including per-expert IQ2 weights."""
+    if qtype_name not in DS4_QUANT_TYPES:
+        raise ValueError(f"not a ds4 quant type: {qtype_name}")
+    if not f32.flags.c_contiguous or f32.dtype != np.float32 or f32.ndim < 1:
+        raise ValueError("ds4 quant input must be a contiguous float32 array")
+    ncols = int(f32.shape[-1])
+    nrows = int(f32.size // ncols)
+    type_id = DS4_QUANT_TYPES[qtype_name]
+    library = load_ds4_quants()
+    library.ds4q_quantize_init(type_id)
+    row_size = int(library.ds4q_row_size(type_id, ncols))
+    if row_size <= 0:
+        raise RuntimeError(f"{qtype_name} does not support row width {ncols}")
+    output = np.empty(nrows * row_size, dtype=np.uint8)
+    requested = int(os.environ.get("QWEN4EXP_QUANT_THREADS", min(64, os.cpu_count() or 1)))
+    source_pointer = ctypes.c_void_p(f32.ctypes.data)
+    output_pointer = ctypes.c_void_p(output.ctypes.data)
+
+    if qtype_name in IMATRIX_REQUIRED_TYPES:
+        if f32.ndim != 3:
+            raise ValueError(
+                f"{qtype_name} input must be [expert, row, column], got {f32.shape}"
+            )
+        expert_count = int(f32.shape[0])
+        rows_per_expert = int(f32.shape[1])
+        if imatrix is None:
+            raise ValueError(f"{qtype_name} requires a per-expert imatrix")
+        if (
+            imatrix.dtype != np.float32
+            or not imatrix.flags.c_contiguous
+            or imatrix.shape != (expert_count, ncols)
+        ):
+            raise ValueError(
+                f"{qtype_name} imatrix must be contiguous float32 "
+                f"[{expert_count}, {ncols}], got {imatrix.shape}/{imatrix.dtype}"
+            )
+        if not np.isfinite(imatrix).all() or (imatrix < 0).any():
+            raise ValueError(f"{qtype_name} imatrix must be finite and non-negative")
+
+        def quantize_expert(expert: int) -> None:
+            start_row = expert * rows_per_expert
+            written = int(
+                library.ds4q_quantize_chunk(
+                    type_id,
+                    source_pointer,
+                    output_pointer,
+                    start_row * ncols,
+                    rows_per_expert,
+                    ncols,
+                    ctypes.c_void_p(imatrix[expert].ctypes.data),
+                )
+            )
+            expected = rows_per_expert * row_size
+            if written != expected:
+                raise RuntimeError(
+                    f"{qtype_name} expert {expert} wrote {written} bytes, expected {expected}"
+                )
+
+        workers = max(1, min(requested, expert_count))
+        with ThreadPoolExecutor(max_workers=workers) as executor:
+            futures = [executor.submit(quantize_expert, expert) for expert in range(expert_count)]
+            for future in futures:
+                future.result()
+    else:
+        if imatrix is not None:
+            raise ValueError(f"{qtype_name} does not accept an imatrix in this converter")
+        workers = max(1, min(requested, nrows))
+        rows_per_worker = (nrows + workers - 1) // workers
+
+        def quantize_range(start_row: int, stop_row: int) -> None:
+            rows = stop_row - start_row
+            written = int(
+                library.ds4q_quantize_chunk(
+                    type_id,
+                    source_pointer,
+                    output_pointer,
+                    start_row * ncols,
+                    rows,
+                    ncols,
+                    None,
+                )
+            )
+            expected = rows * row_size
+            if written != expected:
+                raise RuntimeError(
+                    f"{qtype_name} worker wrote {written} bytes for rows "
+                    f"[{start_row}, {stop_row}), expected {expected}"
+                )
+
+        ranges = [
+            (start, min(nrows, start + rows_per_worker))
+            for start in range(0, nrows, rows_per_worker)
+        ]
+        with ThreadPoolExecutor(max_workers=workers) as executor:
+            futures = [executor.submit(quantize_range, start, stop) for start, stop in ranges]
+            for future in futures:
+                future.result()
+    return output
+
+
+def quantize_ds4_k(f32: np.ndarray, qtype_name: str) -> np.ndarray:
+    """K-quant entry point used by regression tests."""
+    if qtype_name not in {"Q2_K", "Q4_K", "Q5_K", "Q6_K"}:
+        raise ValueError(f"not a ds4 K-quant type: {qtype_name}")
+    return quantize_ds4(f32, qtype_name)
+
+
+def payload_array(
+    row: dict[str, Any],
+    source: torch.Tensor,
+    qtype_name: str,
+    imatrix_entries: dict[str, np.ndarray] | None = None,
+) -> np.ndarray:
+    from gguf import GGMLQuantizationType
+    from gguf.quants import quantize
+
+    if qtype_name == "I64":
+        if source.dtype != torch.int64:
+            raise TypeError(f"{row['name']}: expected source I64, got {source.dtype}")
+        return np.ascontiguousarray(source.numpy(), dtype=np.int64)
+    if source.dtype != torch.bfloat16:
+        raise TypeError(f"{row['name']}: expected source BF16, got {source.dtype}")
+    if qtype_name == "BF16":
+        # BF16 values are copied as their original little-endian 16-bit words.
+        return np.ascontiguousarray(source.view(torch.uint16).numpy(), dtype=np.uint16)
+    f32 = np.ascontiguousarray(source.float().numpy(), dtype=np.float32)
+    if qtype_name == "F32":
+        return f32
+    if qtype_name not in SUPPORTED_PAYLOAD_TYPES:
+        raise NotImplementedError(
+            f"Python payload generation does not support {qtype_name}; use --template-only and the C quantizer"
+        )
+    if qtype_name in DS4_QUANT_TYPES:
+        importance = None
+        if qtype_name in IMATRIX_REQUIRED_TYPES:
+            if imatrix_entries is None:
+                raise RuntimeError(f"{row['name']}: {qtype_name} requires --imatrix")
+            importance = imatrix_for_tensor(row, imatrix_entries)
+        return quantize_ds4(f32, qtype_name, importance)
+    return quantize(f32, GGMLQuantizationType[qtype_name])
+
+
+def existing_outputs(path: Path) -> list[Path]:
+    matches = []
+    if path.exists():
+        matches.append(path)
+    matches.extend(sorted(path.parent.glob(f"{path.stem}-*-of-*.gguf")))
+    return sorted(set(matches))
+
+
+def main() -> int:
+    args = parse_args()
+    if args.sha256sums is not None and not args.hash_output:
+        raise SystemExit("--sha256sums requires --hash-output")
+    from gguf import GGMLQuantizationType, GGUFWriter
+
+    source_map = load_json(args.source_map)
+    ensure_source_pin(source_map)
+    ensure_source_license(args.model_dir)
+    variants = source_map["variants"]
+    if args.variant not in variants:
+        raise SystemExit(f"unknown variant {args.variant!r}; choose one of {variants}")
+    rows: list[dict[str, Any]] = source_map["tensors"]
+    if existing := existing_outputs(args.out):
+        raise SystemExit(f"refusing to overwrite existing output(s): {existing[:3]}")
+    if args.artifact_manifest.exists():
+        raise SystemExit(f"refusing to overwrite {args.artifact_manifest}")
+
+    index = load_json(args.model_dir / "model.safetensors.index.json")
+    weight_map: dict[str, str] = index["weight_map"]
+    missing_index = sorted({row["source_name"] for row in rows} - set(weight_map))
+    if missing_index:
+        raise SystemExit(f"source map has {len(missing_index)} tensors absent from index: {missing_index[:5]}")
+    if not args.template_only:
+        absent = sorted(
+            {
+                weight_map[row["source_name"]]
+                for row in rows
+                if not (args.model_dir / weight_map[row["source_name"]]).is_file()
+            }
+        )
+        if absent:
+            raise SystemExit(f"missing {len(absent)} source shard files: {absent[:5]}")
+
+    payload_bytes = sum(int(row["bytes_by_variant"][args.variant]) for row in rows)
+    target_hist: dict[str, int] = {}
+    for row in rows:
+        qtype = row["target_types"][args.variant]
+        target_hist[qtype] = target_hist.get(qtype, 0) + 1
+    requires_imatrix = any(
+        qtype in IMATRIX_REQUIRED_TYPES for qtype in target_hist
+    )
+    if requires_imatrix and not args.template_only and args.imatrix is None:
+        raise SystemExit(
+            f"variant {args.variant} contains IQ2_XXS payloads and requires --imatrix"
+        )
+    if args.imatrix is not None and not requires_imatrix:
+        raise SystemExit(
+            f"variant {args.variant} has no imatrix-dependent payloads; omit --imatrix"
+        )
+    imatrix_entries: dict[str, np.ndarray] | None = None
+    imatrix_metadata: dict[str, Any] | None = None
+    if args.imatrix is not None:
+        imatrix_entries, imatrix_metadata = load_imatrix(args.imatrix)
+        for row in rows:
+            if row["target_types"][args.variant] in IMATRIX_REQUIRED_TYPES:
+                imatrix_for_tensor(row, imatrix_entries)
+    print(
+        f"plan variant={args.variant} tensors={len(rows)} payload={payload_bytes / 2**30:.6f} GiB "
+        f"types={dict(sorted(target_hist.items()))} template_only={args.template_only}",
+        flush=True,
+    )
+
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    split_max_size = 0 if args.template_only else int(args.split_max_size_gib * 2**30)
+    writer = GGUFWriter(
+        args.out,
+        ARCH,
+        use_temp_file=False,
+        split_max_size=split_max_size,
+    )
+    writer.add_custom_alignment(ALIGNMENT)
+    add_metadata(writer, args.model_dir, args.source_map, args.variant)
+    if imatrix_metadata is not None:
+        add_imatrix_metadata(writer, imatrix_metadata)
+    add_tokenizer(writer, args.model_dir)
+    for row in rows:
+        qtype_name = row["target_types"][args.variant]
+        tensor_dtype = np.dtype(np.int64) if qtype_name == "I64" else np.dtype(np.float32)
+        writer.add_tensor_info(
+            row["name"],
+            row["shape"],
+            tensor_dtype,
+            int(row["bytes_by_variant"][args.variant]),
+            raw_dtype=GGMLQuantizationType[qtype_name],
+        )
+    output_paths = writer.format_shard_names(args.out)
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_ti_data_to_file()
+
+    started = time.time()
+    if not args.template_only:
+        store = ShardStore(args.model_dir, weight_map)
+        for index_row, row in enumerate(rows, 1):
+            qtype_name = row["target_types"][args.variant]
+            source = materialize_source(row, store)
+            payload = payload_array(row, source, qtype_name, imatrix_entries)
+            expected_bytes = int(row["bytes_by_variant"][args.variant])
+            if payload.nbytes != expected_bytes:
+                raise RuntimeError(
+                    f"{row['name']}: generated {payload.nbytes} bytes != planned {expected_bytes}"
+                )
+            writer.write_tensor_data(payload)
+            if index_row == 1 or index_row % 25 == 0 or index_row == len(rows):
+                print(
+                    f"[{index_row}/{len(rows)}] {row['name']} {qtype_name} "
+                    f"{expected_bytes / 2**20:.1f} MiB elapsed={(time.time()-started)/60:.1f} min",
+                    flush=True,
+                )
+            del source, payload
+        store.clear()
+    writer.close()
+
+    files = []
+    for path in output_paths:
+        entry: dict[str, Any] = {"path": str(path), "bytes": path.stat().st_size}
+        if args.hash_output:
+            entry["sha256"] = sha256_file(path)
+        files.append(entry)
+    manifest = {
+        "schema_version": 1,
+        "source": {"repo": SOURCE_REPO, "revision": SOURCE_REVISION},
+        "architecture": ARCH,
+        "variant": args.variant,
+        "template_only": args.template_only,
+        "source_map": str(args.source_map),
+        "source_map_sha256": sha256_file(args.source_map),
+        "gguf_tensor_count": len(rows),
+        "logical_parameters": int(
+            source_map.get(
+                "logical_parameters",
+                sum(int(np.prod(row["shape"], dtype=np.int64)) for row in rows),
+            )
+        ),
+        "planned_payload_bytes": payload_bytes,
+        "target_type_counts": dict(sorted(target_hist.items())),
+        "imatrix": imatrix_metadata,
+        "elapsed_seconds": time.time() - started,
+        "files": files,
+    }
+    args.artifact_manifest.parent.mkdir(parents=True, exist_ok=True)
+    args.artifact_manifest.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+    if args.sha256sums is not None:
+        args.sha256sums.parent.mkdir(parents=True, exist_ok=True)
+        args.sha256sums.write_text(
+            "".join(
+                f"{entry['sha256']}  {Path(entry['path']).name}\n"
+                for entry in files
+            ),
+            encoding="utf-8",
+        )
+    print(f"wrote {len(files)} file(s); manifest -> {args.artifact_manifest}", flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

reproduction/converter/extract_ple_sidecar.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+"""Extract the pinned Qwen4Exp PLE table into verified BF16 raw sidecars."""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import hashlib
+import json
+import os
+import shutil
+import time
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from safetensors import safe_open
+
+
+SOURCE_REPO = "ukisai/Swift1.5-Qwen3.8-Flash-Next"
+SOURCE_REVISION = "0bd4fe22431372cdad1979267d3ab45aa7e6150a"
+CHUNK_BYTES = 32 << 20
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-dir", type=Path, required=True)
+    parser.add_argument("--plan", type=Path, required=True)
+    parser.add_argument("--artifact-root", type=Path, required=True)
+    parser.add_argument("--manifest", type=Path, required=True)
+    parser.add_argument("--report", type=Path, required=True)
+    return parser.parse_args()
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(CHUNK_BYTES), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def update_zeros(digest: Any, count: int) -> None:
+    zero = b"\0" * min(CHUNK_BYTES, max(1, count))
+    while count:
+        take = min(count, len(zero))
+        digest.update(zero[:take])
+        count -= take
+
+
+def write_zeros(handle: Any, digest: Any, count: int) -> None:
+    zero = b"\0" * min(CHUNK_BYTES, max(1, count))
+    while count:
+        take = min(count, len(zero))
+        payload = zero[:take]
+        handle.write(payload)
+        digest.update(payload)
+        count -= take
+
+
+def load_bf16_bytes(model_dir: Path, item: dict[str, Any]) -> tuple[torch.Tensor, memoryview]:
+    path = model_dir / item["source_shard"]
+    if not path.is_file():
+        raise FileNotFoundError(path)
+    with safe_open(str(path), framework="pt", device="cpu") as handle:
+        tensor = handle.get_tensor(item["source_name"])
+    if tensor.dtype != torch.bfloat16:
+        raise TypeError(f"{item['source_name']}: expected BF16, got {tensor.dtype}")
+    expected_shape = (int(item["rows"]), int(item["embedding_row_dimension"]))
+    if tuple(tensor.shape) != expected_shape:
+        raise ValueError(f"{item['source_name']}: {tuple(tensor.shape)} != {expected_shape}")
+    tensor = tensor.contiguous()
+    array = np.ascontiguousarray(tensor.view(torch.uint16).numpy(), dtype="<u2")
+    payload = memoryview(array).cast("B")
+    if len(payload) != int(item["payload_bytes"]):
+        raise ValueError(f"{item['source_name']}: {len(payload)} != {item['payload_bytes']}")
+    return tensor, payload
+
+
+def main() -> int:
+    args = parse_args()
+    plan = json.loads(args.plan.read_text())
+    if plan.get("source") != {"repo": SOURCE_REPO, "revision": SOURCE_REVISION}:
+        raise SystemExit(f"unexpected source pin: {plan.get('source')}")
+    if plan.get("storage_dtype") != "BF16" or plan.get("checksum_status") != "pending_extraction":
+        raise SystemExit("plan must be a pending BF16 extraction manifest")
+
+    physical_files: list[dict[str, Any]] = plan["physical_files"]
+    logical_parts: list[dict[str, Any]] = plan["logical_parts"]
+    required = int(plan["total_file_bytes_including_alignment"])
+    available = shutil.disk_usage(args.artifact_root.parent).free
+    if available < required + (8 << 30):
+        raise SystemExit(f"insufficient free space: need {required + (8 << 30)}, have {available}")
+    if args.manifest.exists() or args.report.exists():
+        raise SystemExit("refusing to overwrite an existing manifest/report")
+    for entry in physical_files:
+        final_path = args.artifact_root / entry["path"]
+        partial_path = final_path.with_suffix(final_path.suffix + ".partial")
+        if final_path.exists() or partial_path.exists():
+            raise SystemExit(f"refusing to overwrite {final_path} or {partial_path}")
+
+    started = time.time()
+    result = copy.deepcopy(plan)
+    result_parts = {int(item["logical_part"]): item for item in result["logical_parts"]}
+    result_files = {int(item["index"]): item for item in result["physical_files"]}
+    expected_file_hashes: dict[int, str] = {}
+
+    for file_entry in physical_files:
+        file_index = int(file_entry["index"])
+        final_path = args.artifact_root / file_entry["path"]
+        partial_path = final_path.with_suffix(final_path.suffix + ".partial")
+        final_path.parent.mkdir(parents=True, exist_ok=True)
+        selected = sorted(
+            (item for item in logical_parts if int(item["physical_file_index"]) == file_index),
+            key=lambda item: int(item["file_offset"]),
+        )
+        expected_digest = hashlib.sha256()
+        with partial_path.open("xb") as output:
+            position = 0
+            for item in selected:
+                offset = int(item["file_offset"])
+                if offset < position:
+                    raise RuntimeError(f"overlapping logical part {item['logical_part']}")
+                if offset > position:
+                    write_zeros(output, expected_digest, offset - position)
+                    position = offset
+                tensor, payload = load_bf16_bytes(args.model_dir, item)
+                part_digest = hashlib.sha256()
+                for start in range(0, len(payload), CHUNK_BYTES):
+                    chunk = payload[start : start + CHUNK_BYTES]
+                    output.write(chunk)
+                    expected_digest.update(chunk)
+                    part_digest.update(chunk)
+                position += len(payload)
+                result_parts[int(item["logical_part"])]["sha256"] = part_digest.hexdigest()
+                elapsed = max(time.time() - started, 1e-6)
+                done = sum(
+                    int(result_parts[index]["payload_bytes"])
+                    for index in result_parts
+                    if result_parts[index].get("sha256")
+                )
+                print(
+                    f"PLE part {int(item['logical_part']) + 1:03d}/128 "
+                    f"{done / 2**30:.2f} GiB {done / elapsed / 2**20:.1f} MiB/s",
+                    flush=True,
+                )
+                del tensor, payload
+            final_size = int(file_entry["file_bytes"])
+            if position < final_size:
+                write_zeros(output, expected_digest, final_size - position)
+                position = final_size
+            if position != final_size:
+                raise RuntimeError(f"{partial_path}: wrote {position}, expected {final_size}")
+            output.flush()
+            os.fsync(output.fileno())
+        expected_hash = expected_digest.hexdigest()
+        actual_hash = sha256_file(partial_path)
+        if actual_hash != expected_hash:
+            raise RuntimeError(f"{partial_path}: {actual_hash} != expected {expected_hash}")
+        os.replace(partial_path, final_path)
+        expected_file_hashes[file_index] = expected_hash
+        result_files[file_index]["sha256"] = expected_hash
+        result_files[file_index]["verified_bytes"] = final_path.stat().st_size
+
+    result["logical_parts"] = [result_parts[index] for index in sorted(result_parts)]
+    result["physical_files"] = [result_files[index] for index in sorted(result_files)]
+    result["checksum_status"] = "verified_source_bf16_and_physical_files"
+    result["validation"] = {
+        "source_dtype": "BF16",
+        "source_shape_checked": True,
+        "source_payload_sha256_per_logical_part": True,
+        "expected_composite_sha256_matches_reread_file_sha256": True,
+        "all_physical_file_sizes_match": True,
+    }
+    result["elapsed_seconds"] = time.time() - started
+    args.manifest.parent.mkdir(parents=True, exist_ok=True)
+    args.manifest.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n")
+    report = {
+        "schema_version": 1,
+        "ok": True,
+        "source": result["source"],
+        "manifest": str(args.manifest),
+        "manifest_sha256": sha256_file(args.manifest),
+        "physical_files": result["physical_files"],
+        "logical_part_count": len(result["logical_parts"]),
+        "total_payload_bytes": result["total_payload_bytes"],
+        "total_file_bytes_including_alignment": result["total_file_bytes_including_alignment"],
+        "elapsed_seconds": result["elapsed_seconds"],
+    }
+    args.report.parent.mkdir(parents=True, exist_ok=True)
+    args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n")
+    print(f"wrote verified manifest {args.manifest}", flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

reproduction/converter/port_source_map.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""Port the audited Qwen3.8 source map to an architecture-identical checkpoint.
+
+The tensor roles, slices, names, shapes, and precision targets are retained.
+Only the source provenance and physical safetensor shard locations are changed,
+and every referenced tensor is checked against the destination index.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--template", type=Path, required=True)
+    parser.add_argument("--model-dir", type=Path, required=True)
+    parser.add_argument("--source-repo", required=True)
+    parser.add_argument("--source-revision", required=True)
+    parser.add_argument("--out", type=Path, required=True)
+    return parser.parse_args()
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(16 << 20), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def main() -> int:
+    args = parse_args()
+    if args.out.exists():
+        raise SystemExit(f"refusing to overwrite {args.out}")
+    source_map: dict[str, Any] = json.loads(args.template.read_text())
+    index = json.loads((args.model_dir / "model.safetensors.index.json").read_text())
+    weight_map: dict[str, str] = index["weight_map"]
+    missing = sorted({row["source_name"] for row in source_map["tensors"]} - set(weight_map))
+    if missing:
+        raise SystemExit(f"destination index is missing {len(missing)} tensors: {missing[:8]}")
+
+    original_source = source_map.get("source")
+    template_sha256 = sha256(args.template)
+    changed_shards = 0
+    for row in source_map["tensors"]:
+        destination_shard = weight_map[row["source_name"]]
+        if row.get("source_shard") != destination_shard:
+            changed_shards += 1
+        row["source_shard"] = destination_shard
+
+    source_map["source"] = {
+        "repo": args.source_repo,
+        "revision": args.source_revision,
+    }
+    source_map["recipe_template"] = {
+        "source": original_source,
+        "sha256": template_sha256,
+        "path": str(args.template),
+        "unchanged_tensor_recipe": True,
+    }
+    source_map["port_audit"] = {
+        "referenced_rows": len(source_map["tensors"]),
+        "referenced_source_tensors": len({row["source_name"] for row in source_map["tensors"]}),
+        "missing_destination_tensors": 0,
+        "source_shard_locations_changed": changed_shards,
+    }
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.out.write_text(json.dumps(source_map, indent=2, sort_keys=True) + "\n")
+    print(
+        f"ported {len(source_map['tensors'])} rows / "
+        f"{len({row['source_name'] for row in source_map['tensors']})} source tensors; "
+        f"changed shard locations={changed_shards}"
+    )
+    print(f"wrote {args.out}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

reproduction/manifests/reference/base-source-info.json

@@ -0,0 +1,1475 @@
+{
+  "_id": "6a8bffdb39eb84b2b2755a6e",
+  "id": "Qwen/Qwen3.8-Flash-Next",
+  "private": false,
+  "pipeline_tag": "image-text-to-text",
+  "library_name": "transformers",
+  "tags": [
+    "transformers",
+    "safetensors",
+    "qwen4_exp",
+    "image-text-to-text",
+    "conversational",
+    "license:other",
+    "eval-results",
+    "endpoints_compatible",
+    "region:us"
+  ],
+  "downloads": 1073493,
+  "likes": 5711,
+  "modelId": "Qwen/Qwen3.8-Flash-Next",
+  "author": "Qwen",
+  "sha": "f5d08274bafd880402bd16f5e3e6c514136ec06c",
+  "lastModified": "2026-08-26T12:29:54.000Z",
+  "gated": false,
+  "disabled": false,
+  "widgetData": [
+    {
+      "text": "Hi, what can you help me with?"
+    },
+    {
+      "text": "What is 84 * 3 / 2?"
+    },
+    {
+      "text": "Tell me an interesting fact about the universe!"
+    },
+    {
+      "text": "Explain quantum computing in simple terms."
+    }
+  ],
+  "model-index": null,
+  "config": {
+    "architectures": [
+      "Qwen4ExpForConditionalGeneration"
+    ],
+    "model_type": "qwen4_exp",
+    "tokenizer_config": {
+      "bos_token": null,
+      "chat_template": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n    {%- if content is string %}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping %}\n        {%- for item in content %}\n            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain images.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n            {%- elif 'video' in item or item.type == 'video' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain videos.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n            {%- elif 'text' in item %}\n                {{- item.text }}\n            {%- else %}\n                {{- raise_exception('Unexpected item type in content.') }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif content is none or content is undefined %}\n        {{- '' }}\n    {%- else %}\n        {{- raise_exception('Unexpected content type.') }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n    {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- set reasoning_instructions = '' %}\n{%- if enable_thinking is undefined or enable_thinking is true %}\n    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}\n    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}\n        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}\n    {%- endif %}\n    {%- if resolved_reasoning_effort == 'xhigh' %}\n        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}\n    {%- elif resolved_reasoning_effort == 'low' %}\n        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}\n    {%- endif %}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if reasoning_instructions %}\n        {{- reasoning_instructions + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '\\n\\n' + content }}\n        {%- endif %}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '<|im_start|>system\\n' + (reasoning_instructions + '\\n\\n' if reasoning_instructions else '')  + content + '<|im_end|>\\n' }}\n        {%- elif reasoning_instructions %}\n            {{- '<|im_start|>system\\n' + reasoning_instructions + '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- elif reasoning_instructions %}\n        {{- '<|im_start|>system\\n' + reasoning_instructions + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" %}\n        {%- set content = render_content(message.content, false)|trim %}\n        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n            {%- set ns.multi_step_tool = false %}\n            {%- set ns.last_query_index = index %}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n    {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n    {%- set content = render_content(message.content, true)|trim %}\n    {%- if message.role == \"system\" %}\n        {%- if not loop.first %}\n            {{- raise_exception('System message must be at the beginning.') }}\n        {%- endif %}\n    {%- elif message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- endif %}\n        {%- set reasoning_content = reasoning_content|trim %}\n        {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function is defined %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {%- if loop.first %}\n                    {%- if content|trim %}\n                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- else %}\n                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                {%- endif %}\n                {%- if tool_call.arguments is defined and tool_call.arguments != '' %}\n                    {%- for args_name, args_value in tool_call.arguments|items %}\n                        {{- '<parameter=' + args_name + '>\\n' }}\n                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n                        {{- args_value }}\n                        {{- '\\n</parameter>\\n' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- raise_exception('Unexpected message role.') }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- else %}\n        {{- '<think>\\n' }}\n    {%- endif %}\n{%- endif %}",
+      "eos_token": "<|im_end|>",
+      "pad_token": "<|endoftext|>",
+      "unk_token": null
+    },
+    "chat_template_jinja": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n    {%- if content is string %}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping %}\n        {%- for item in content %}\n            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain images.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set image_count.value = image_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n            {%- elif 'video' in item or item.type == 'video' %}\n                {%- if is_system_content %}\n                    {{- raise_exception('System message cannot contain videos.') }}\n                {%- endif %}\n                {%- if do_vision_count %}\n                    {%- set video_count.value = video_count.value + 1 %}\n                {%- endif %}\n                {%- if add_vision_id %}\n                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n                {%- endif %}\n                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n            {%- elif 'text' in item %}\n                {{- item.text }}\n            {%- else %}\n                {{- raise_exception('Unexpected item type in content.') }}\n            {%- endif %}\n        {%- endfor %}\n    {%- elif content is none or content is undefined %}\n        {{- '' }}\n    {%- else %}\n        {{- raise_exception('Unexpected content type.') }}\n    {%- endif %}\n{%- endmacro %}\n{%- if not messages %}\n    {{- raise_exception('No messages provided.') }}\n{%- endif %}\n{%- set reasoning_instructions = '' %}\n{%- if enable_thinking is undefined or enable_thinking is true %}\n    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}\n    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}\n        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}\n    {%- endif %}\n    {%- if resolved_reasoning_effort == 'xhigh' %}\n        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}\n    {%- elif resolved_reasoning_effort == 'low' %}\n        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}\n    {%- endif %}\n{%- endif %}\n{%- if tools and tools is iterable and tools is not mapping %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if reasoning_instructions %}\n        {{- reasoning_instructions + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '\\n\\n' + content }}\n        {%- endif %}\n    {%- endif %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {%- set content = render_content(messages[0].content, false, true)|trim %}\n        {%- if content %}\n            {{- '<|im_start|>system\\n' + (reasoning_instructions + '\\n\\n' if reasoning_instructions else '')  + content + '<|im_end|>\\n' }}\n        {%- elif reasoning_instructions %}\n            {{- '<|im_start|>system\\n' + reasoning_instructions + '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- elif reasoning_instructions %}\n        {{- '<|im_start|>system\\n' + reasoning_instructions + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" %}\n        {%- set content = render_content(message.content, false)|trim %}\n        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n            {%- set ns.multi_step_tool = false %}\n            {%- set ns.last_query_index = index %}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if ns.multi_step_tool %}\n    {{- raise_exception('No user query found in messages.') }}\n{%- endif %}\n{%- for message in messages %}\n    {%- set content = render_content(message.content, true)|trim %}\n    {%- if message.role == \"system\" %}\n        {%- if not loop.first %}\n            {{- raise_exception('System message must be at the beginning.') }}\n        {%- endif %}\n    {%- elif message.role == \"user\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- endif %}\n        {%- set reasoning_content = reasoning_content|trim %}\n        {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if tool_call.function is defined %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {%- if loop.first %}\n                    {%- if content|trim %}\n                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- else %}\n                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                    {%- endif %}\n                {%- else %}\n                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n                {%- endif %}\n                {%- if tool_call.arguments is defined and tool_call.arguments != '' %}\n                    {%- for args_name, args_value in tool_call.arguments|items %}\n                        {{- '<parameter=' + args_name + '>\\n' }}\n                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n                        {{- args_value }}\n                        {{- '\\n</parameter>\\n' }}\n                    {%- endfor %}\n                {%- endif %}\n                {{- '</function>\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- raise_exception('Unexpected message role.') }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- else %}\n        {{- '<think>\\n' }}\n    {%- endif %}\n{%- endif %}"
+  },
+  "cardData": {
+    "library_name": "transformers",
+    "license": "other",
+    "license_name": "qwen-community-1.0",
+    "license_link": "LICENSE",
+    "pipeline_tag": "image-text-to-text"
+  },
+  "transformersInfo": {
+    "auto_model": "AutoModelForMultimodalLM",
+    "pipeline_tag": "image-text-to-text",
+    "processor": "AutoProcessor"
+  },
+  "siblings": [
+    {
+      "rfilename": ".gitattributes",
+      "blobId": "52373fe24473b1aa44333d318f578ae6bf04b49b",
+      "size": 1570
+    },
+    {
+      "rfilename": "LICENSE",
+      "blobId": "9557a8961782792814714b7488346732c043793d",
+      "size": 3235
+    },
+    {
+      "rfilename": "README.md",
+      "blobId": "c0d8fa2586c99fc9c6af9c13538aafd6e99494fe",
+      "size": 64796
+    },
+    {
+      "rfilename": "chat_template.jinja",
+      "blobId": "c0c686f9c38d70d179fb7b5f5aa7530bc913dda3",
+      "size": 8952
+    },
+    {
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+      "blobId": "491017e9980e44ef01afa2c4782f5c7e169b9b26",
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+      "blobId": "023756cfadf88e5bf69eefeee3e172f38c448d64",
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reproduction/manifests/reference/card-reference.json

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reproduction/manifests/reference/fp8-ple-manifest.json

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