STRATA Native LM: non-commercial release

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BASE_MODEL.json

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+{
+  "model": "Qwen/Qwen3-4B-Instruct-2507",
+  "revision": "cdbee75f17c01a7cc42f958dc650907174af0554",
+  "weights_included": false
+}

LICENSE

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+STRATA Native LM v1
+
+The STRATA-authored model artifacts, runtime, and documentation in this
+release are licensed under Creative Commons Attribution-NonCommercial
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MANIFEST.sha256

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README.md

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+---
+license: cc-by-nc-4.0
+library_name: transformers
+base_model: Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+tags:
+  - strata
+  - persistent-memory
+  - structured-memory
+  - neuro-symbolic
+  - exact-value-copying
+---
+
+# STRATA Native LM v1
+
+Licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) for non-commercial use. Commercial use requires separate permission from the rights holders. The upstream base model and third-party dependencies retain their respective licenses.
+
+STRATA Native LM couples a frozen Qwen3-4B-Instruct-2507 backbone to a trained 20.44M-parameter memory interface. It generates answers from trusted structured observations supplied by tools, APIs, or applications. Structured writes and read addresses are supplied explicitly.
+
+```text
+structured observation
+  -> WRITE(instance, predicate, role, value)
+  -> persistent structured memory
+  -> memory-conditioned language model
+  -> generated frame + exact value copying
+```
+
+The model receives the current query and compact memory without previous source tokens, source-history KV-cache entries, or persisted source residuals. Stored value bytes are copied only after the response frame is complete. Integrity checks reject invalid, stale, deleted, or corrupted references.
+
+## Results
+
+| Metric | Result |
+| --- | ---: |
+| Atomic writes in state history | 2,004,344 |
+| Transactions | 564,312 |
+| Unique base records | 4,096 |
+| Evaluations across ages and packings | 61,440 |
+| Persistent-memory horizon | 128 independent 8,192-token windows |
+| Factual accuracy | 1.000 |
+| Copied-value byte correctness | 1.000 |
+| Held-out-schema accuracy | 1.000 |
+| 2/3/4-hop join accuracy | 1.000 |
+| Field-specific intervention following | 1.000 |
+| Integrity verification and execution replay | 1.000 |
+| Supporting proof-inclusive retained-state reductions | 86.46% / 86.72% |
+| Neural value-state reduction | 95.83% (128 vs. 3,072 bytes/value) |
+
+The neural-state figure excludes application value bytes and shared address/integrity metadata. The structured-service accounting includes those bytes. The horizon is persistence across independent windows, not a single Transformer context.
+
+STRATA and the realization-matched token-value arm both achieved 1.000 factual accuracy. On eight NVIDIA L40 GPUs, the evaluated implementation took 43.17 ms/record versus 564.82 ms/record for the source-history arm named `FULL_KV`. Both decoding loops disabled KV reuse; the latter used free generation. These timings describe that workload and implementation.
+
+## Install
+
+Requires Python 3.11 or newer and a CUDA-capable PyTorch installation. A single NVIDIA L40 with 46 GB memory was used for the release inference check.
+
+```bash
+python3 -m pip install huggingface_hub
+hf download nur-dev/strata-native-lm --local-dir strata-native-lm-v1
+cd strata-native-lm-v1
+python3 verify.py
+python3 -m pip install -r requirements.txt
+python3 -m pip install --no-deps wheels/strata-0.1.0.post2-py3-none-any.whl
+hf download Qwen/Qwen3-4B-Instruct-2507 \
+  --revision cdbee75f17c01a7cc42f958dc650907174af0554 \
+  --local-dir base-model
+export STRATA_BASE_MODEL="$PWD/base-model"
+```
+
+## Run
+
+This example constructs one structured observation and reads it without putting its value in the query.
+
+```bash
+python3 load_and_answer.py \
+  --event calendar:meeting_283 \
+  --predicate meeting \
+  --role location \
+  --payload-handle 17 \
+  --payload "North Campus Room 17" \
+  --query "Where is the meeting?" \
+  --device cuda:0
+```
+
+The JSON response contains `answer`, `frame`, `status`, `payload_handle`, `receipt`, and timing fields. Successful output contains the stored value `North Campus Room 17` verbatim; surrounding wording is generated by the model.
+
+## Files
+
+| Path | Contents |
+| --- | --- |
+| `checkpoints/` | Compact-memory reader, memory interface, and copy-action head |
+| `configs/` | Inference settings |
+| `evaluation.json` | Evaluation metrics, criteria, and source-artifact hashes |
+| `wheels/` | Runtime package |
+| `BASE_MODEL.json` | Required upstream model and immutable revision |
+| `load_and_answer.py` | Executable structured-read example |
+| `verify.py`, `MANIFEST.sha256` | Local file-integrity verification |
+
+The base model is downloaded from its upstream Hugging Face repository; its weights are not duplicated here.

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configs/strata_native_lm_integration_m1.json

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+{
+  "model": {
+    "backbone": "Qwen3-4B-Instruct-2507",
+    "backbone_trainable": false,
+    "precision": "bfloat16",
+    "attention_implementation": "sdpa",
+    "gradient_checkpointing": true,
+    "memory_port_layers": [
+      2,
+      5,
+      8,
+      11,
+      14,
+      17,
+      20,
+      23,
+      26,
+      29,
+      32,
+      35
+    ],
+    "compact_width": 64,
+    "address_width": 48,
+    "memory_width": 256,
+    "memory_tokens": 4,
+    "attention_width": 256,
+    "attention_heads": 4,
+    "adapter_rank": 32,
+    "payload_classes": 255,
+    "auxiliary_payload_loss_weight": 0.5
+  },
+  "substrate": {
+    "payload_width": 8
+  }
+}

configs/strata_native_lm_p0_m2_v1.json

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+{
+  "model": {
+    "base_vocab_size": 48029,
+    "vocab_size": 48037,
+    "d_model": 192,
+    "d_ff": 512,
+    "num_layers": 4,
+    "num_heads": 6,
+    "max_position_embeddings": 128,
+    "dropout": 0.0,
+    "initializer_range": 0.02,
+    "auxiliary_write_weight": 0.5,
+    "tie_word_embeddings": true,
+    "address_code_width": 16,
+    "payload_width": 8,
+    "memory_dim": 64
+  }
+}

configs/strata_native_lm_system_v1.json

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+{
+  "frame": {
+    "canonical_frame_handle": 1,
+    "canonical_frame_surrogate": "record-001",
+    "structural_terminator": ".",
+    "actual_payload_state_in_frame_forward": false,
+    "materialization_after_frame_complete": true
+  },
+  "evaluation": {
+    "max_actions": 32
+  }
+}

evaluation.json

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+{
+  "system": "STRATA Native LM v1",
+  "status": "QUALIFIED",
+  "checks": {
+    "actual_payload_tokens_in_frame_forward": true,
+    "answer_queries": true,
+    "causal_intervention_following": true,
+    "config_identity": true,
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+              "total": 7935
+            }
+          },
+          "value_type": {
+            "datetime": {
+              "accuracy": 1.0,
+              "correct": 4905,
+              "total": 4905
+            },
+            "entity": {
+              "accuracy": 1.0,
+              "correct": 25770,
+              "total": 25770
+            },
+            "enum": {
+              "accuracy": 1.0,
+              "correct": 15060,
+              "total": 15060
+            },
+            "integer": {
+              "accuracy": 1.0,
+              "correct": 5190,
+              "total": 5190
+            },
+            "location": {
+              "accuracy": 1.0,
+              "correct": 7230,
+              "total": 7230
+            },
+            "string": {
+              "accuracy": 1.0,
+              "correct": 3285,
+              "total": 3285
+            }
+          }
+        },
+        "statuses": {
+          "FRAME_COMPLETE": 61440
+        },
+        "total": 61440,
+        "wrong_accepted": 0
+      },
+      "TOKEN_VALUE": {
+        "accepted": 61440,
+        "correct": 61440,
+        "factual_accuracy": 1.0,
+        "latency": {
+          "chunk_ms_per_record_p50": 41.69859297689982,
+          "chunk_ms_per_record_p95": 43.74561276199529,
+          "cluster_records_per_second": 182.97732840097063,
+          "end_to_end_ms_per_record": 43.72126355713893
+        },
+        "payload_exact": 61440,
+        "payload_exact_accuracy": 1.0,
+        "slices": {
+          "age_windows": {
+            "1": {
+              "accuracy": 1.0,
+              "correct": 12288,
+              "total": 12288
+            },
+            "127": {
+              "accuracy": 1.0,
+              "correct": 12288,
+              "total": 12288
+            },
+            "32": {
+              "accuracy": 1.0,
+              "correct": 12288,
+              "total": 12288
+            },
+            "64": {
+              "accuracy": 1.0,
+              "correct": 12288,
+              "total": 12288
+            },
+            "8": {
+              "accuracy": 1.0,
+              "correct": 12288,
+              "total": 12288
+            }
+          },
+          "graph_depth": {
+            "1": {
+              "accuracy": 1.0,
+              "correct": 49920,
+              "total": 49920
+            },
+            "2": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            },
+            "3": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            },
+            "4": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            }
+          },
+          "hard_slice": {
+            "date_time": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "fresh_random_string": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "location_with_spaces": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "long_numeric": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "mixed_alphanumeric": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "near_neighbor_identifier": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "person_name": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "shared_long_prefix": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "unicode": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "uuid_like": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "values_with_spaces": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            },
+            "variable_length": {
+              "accuracy": 1.0,
+              "correct": 960,
+              "total": 960
+            }
+          },
+          "kind": {
+            "binding": {
+              "accuracy": 1.0,
+              "correct": 38400,
+              "total": 38400
+            },
+            "heldout": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            },
+            "join": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            }
+          },
+          "operation": {
+            "join_2_hop": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            },
+            "join_3_hop": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            },
+            "join_4_hop": {
+              "accuracy": 1.0,
+              "correct": 3840,
+              "total": 3840
+            },
+            "point": {
+              "accuracy": 1.0,
+              "correct": 37365,
+              "total": 37365
+            },
+            "resolved_join": {
+              "accuracy": 1.0,
+              "correct": 2880,
+              "total": 2880
+            },
+            "temporal": {
+              "accuracy": 1.0,
+              "correct": 9675,
+              "total": 9675
+            }
+          },
+          "packing": {
+            "20261011": {
+              "accuracy": 1.0,
+              "correct": 20480,
+              "total": 20480
+            },
+            "20261012": {
+              "accuracy": 1.0,
+              "correct": 20480,
+              "total": 20480
+            },
+            "20261013": {
+              "accuracy": 1.0,
+              "correct": 20480,
+              "total": 20480
+            }
+          },
+          "schema": {
+            "approval": {
+              "accuracy": 1.0,
+              "correct": 3705,
+              "total": 3705
+            },
+            "calendar": {
+              "accuracy": 1.0,
+              "correct": 7680,
+              "total": 7680
+            },
+            "crm": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            },
+            "deployment": {
+              "accuracy": 1.0,
+              "correct": 3930,
+              "total": 3930
+            },
+            "incident": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            },
+            "inventory": {
+              "accuracy": 1.0,
+              "correct": 7680,
+              "total": 7680
+            },
+            "project": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            },
+            "shipment": {
+              "accuracy": 1.0,
+              "correct": 3885,
+              "total": 3885
+            }
+          },
+          "schema_partition": {
+            "heldout": {
+              "accuracy": 1.0,
+              "correct": 11520,
+              "total": 11520
+            },
+            "registered": {
+              "accuracy": 1.0,
+              "correct": 49920,
+              "total": 49920
+            }
+          },
+          "updated": {
+            "False": {
+              "accuracy": 1.0,
+              "correct": 53505,
+              "total": 53505
+            },
+            "True": {
+              "accuracy": 1.0,
+              "correct": 7935,
+              "total": 7935
+            }
+          },
+          "value_type": {
+            "datetime": {
+              "accuracy": 1.0,
+              "correct": 4905,
+              "total": 4905
+            },
+            "entity": {
+              "accuracy": 1.0,
+              "correct": 25770,
+              "total": 25770
+            },
+            "enum": {
+              "accuracy": 1.0,
+              "correct": 15060,
+              "total": 15060
+            },
+            "integer": {
+              "accuracy": 1.0,
+              "correct": 5190,
+              "total": 5190
+            },
+            "location": {
+              "accuracy": 1.0,
+              "correct": 7230,
+              "total": 7230
+            },
+            "string": {
+              "accuracy": 1.0,
+              "correct": 3285,
+              "total": 3285
+            }
+          }
+        },
+        "statuses": {
+          "FRAME_COMPLETE": 61440
+        },
+        "total": 61440,
+        "wrong_accepted": 0
+      }
+    },
+    "claim_boundary": {
+      "authoritative_structured_observations": true,
+      "autonomous_natural_language_writes": false,
+      "frame_separated_exact_payload_realization": true,
+      "historical_transformer_context": false,
+      "natural_language_answers": true,
+      "one_million_token_transformer_attention_context": false,
+      "resolved_join_result_is_an_authoritative_strata_value": true
+    },
+    "controls": {
+      "corrupt_state_fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "deleted_fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "event_following": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "invalid_receipt_fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "invalid_slot_fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "payload_following": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "payload_frame_invariance": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "predicate_following": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "role_following": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "stale_fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2048,
+        "total": 2048
+      },
+      "state_shuffled_factual": {
+        "accuracy": 0.0,
+        "correct": 0,
+        "total": 2048
+      },
+      "state_zero_factual": {
+        "accuracy": 0.0,
+        "correct": 0,
+        "total": 2048
+      }
+    },
+    "deletion": {
+      "fail_closed": {
+        "accuracy": 1.0,
+        "correct": 2000,
+        "total": 2000
+      }
+    },
+    "identities": {
+      "config_sha256": "0ad77569ac1fd20e8a74c322a21f43ff2cff7139fdee341e936eb285a23d20f2",
+      "m1_checkpoint_sha256": "75b595cafc25cb87bcfd0fdac33965ba5efaf80f691c802549693fe127c9cf7b",
+      "m3_action_head_sha256": "6f0a5f6bbb660fbdbdd2695bdd93b9e5c53d4fcb79cb4d16a3f62a1b7bbbac9e"
+    },
+    "isolation": {
+      "actual_payload_tokens_in_frame_forward": 0,
+      "historical_ordinary_token_kv": 0,
+      "no_trigger_base_logit_delta": 0.0,
+      "persisted_source_residual": 0,
+      "source_tokens_in_strata_prompt": 0,
+      "target_leakage": 0
+    },
+    "logical_horizon": {
+      "ages_windows": [
+        1,
+        8,
+        32,
+        64,
+        127
+      ],
+      "packings": [
+        20261011,
+        20261012,
+        20261013
+      ],
+      "tokens": 1048576
+    },
+    "packet": {
+      "semantic_sha256": "9fd42fb9e2cc58c2cb563c08a0cb4c5ea31c8b19ad99aca5880d025eaf12cdb7",
+      "sha256": "f5d932b6c8694cdc51ff804dc30d82782e4d5c6438ec4bb3c9a67abf4cd124df"
+    },
+    "parameters": {
+      "m1_memory_path": 20438651,
+      "m3_action_head": 10242,
+      "updates_during_system_qualification": 0
+    },
+    "persisted_state_history": {
+      "active_bindings": 1400040,
+      "committed_atomic_writes": 2004344,
+      "schemas": [
+        "calendar",
+        "crm",
+        "incident",
+        "inventory",
+        "project"
+      ],
+      "transactions": 564312
+    },
+    "records": {
+      "answer_queries": 61440,
+      "causal": 2048,
+      "deletion_controls": 2000,
+      "unique_base": 4096
+    },
+    "schema": "STRATA_NATIVE_LM_SYSTEM_V1_EVALUATION_V1",
+    "status": "EVALUATION_COMPLETE",
+    "substrate": {
+      "compact_state_bytes_per_value": 128,
+      "deleted_state_replay": 1.0,
+      "proof_runtime_replay": 1.0,
+      "token_value_bytes_per_value": 3072,
+      "total": 4096,
+      "value_state_reduction": 0.9583333333333334,
+      "verified": 4096
+    },
+    "world_size": 8
+  },
+  "decoding": {
+    "use_cache": false,
+    "full_kv_realization": "free generation",
+    "strata_and_token_value_realization": "two-stage frame generation and value copying"
+  },
+  "retained_state_accounting": {
+    "long_horizon": {
+      "compact_total_retained_bytes": 245149935,
+      "token_value_total_retained_bytes": 1810716143,
+      "total_retained_state_reduction": 0.864611614610209
+    },
+    "structured_service": {
+      "compact_total_retained_bytes": 630624401,
+      "token_value_total_retained_bytes": 4752342161,
+      "minimum_total_retained_state_reduction": 0.8671911839607945,
+      "authority_commitment_bytes": 451419281,
+      "database_bytes": 2890657792
+    },
+    "neural_value_bytes": {
+      "strata": 128,
+      "token_value": 3072
+    }
+  },
+  "source_sha256": {
+    "registration": "f03959ee3d1b3ce8a499e59c31a9474c281c01a63ed3c53deeb18ecba039cf93",
+    "evaluation": "30145b51efe75371ab017cbba1fd4a592fdc803f165598381a61f7bb41e4e984",
+    "adjudication": "cca87e0cef2c788cca7cb972bdf45dcc81775e565af554b135f17d5738fb1427",
+    "long_horizon": "0969732959aadcb68d8c1562348c81bc74e77114e7ebd8d216bda74c9c3c4f38",
+    "structured_service": "a6871c0cddecfb6aea93d38252de9c5251172e2e44fbf533402ef9d0b6a23829"
+  }
+}

load_and_answer.py

@@ -0,0 +1,169 @@
+#!/usr/bin/env python3
+"""Run one source-free STRATA Native LM v1 authoritative read."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from strata.data.native_lm_integration import NativeLMExample, address_codes
+from strata.eval.native_lm_frame_separated_copy import frame_separated_generate
+from strata.memory_model.codec import FrozenMemoryCodec
+from strata.modeling.exact_payload_realizer import PayloadAuthority
+from strata.modeling.native_lm_integration import (
+    QualifiedP0M2Reader,
+    StrataMemoryConditionedLM,
+)
+from strata.modeling.structural_copy import StructuralCopyActionHead
+from strata.training.native_lm_integration import compact_state_table
+
+
+def load_model(root: Path, base_model: str, device: torch.device):
+    config = json.loads((root / "configs/strata_native_lm_system_v1.json").read_text())
+    m1_config = json.loads(
+        (root / "configs/strata_native_lm_integration_m1.json").read_text()
+    )
+    model_config = m1_config["model"]
+    tokenizer = AutoTokenizer.from_pretrained(base_model, local_files_only=True)
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    backbone = AutoModelForCausalLM.from_pretrained(
+        base_model,
+        local_files_only=True,
+        torch_dtype=torch.bfloat16,
+        attn_implementation=model_config["attention_implementation"],
+    ).to(device)
+    backbone.config.use_cache = False
+    codec = FrozenMemoryCodec(
+        checkpoint_path=root / "checkpoints/P0_M2_CHECKPOINT_FINAL.pt",
+        config_path=root / "configs/strata_native_lm_p0_m2_v1.json",
+        device="cpu",
+    )
+    model = StrataMemoryConditionedLM(
+        backbone,
+        qualified_reader=QualifiedP0M2Reader(codec.model),
+        layer_indices=model_config["memory_port_layers"],
+        compact_width=int(model_config["compact_width"]),
+        address_width=int(model_config["address_width"]),
+        payload_width=int(m1_config["substrate"]["payload_width"]),
+        memory_width=int(model_config["memory_width"]),
+        memory_tokens=int(model_config["memory_tokens"]),
+        attention_width=int(model_config["attention_width"]),
+        heads=int(model_config["attention_heads"]),
+        adapter_rank=int(model_config["adapter_rank"]),
+        payload_classes=int(model_config["payload_classes"]),
+        auxiliary_payload_loss_weight=float(
+            model_config["auxiliary_payload_loss_weight"]
+        ),
+    ).to(device)
+    checkpoint = torch.load(
+        root / "checkpoints/MEMORY_PATH_FINAL.pt",
+        map_location="cpu",
+        weights_only=False,
+    )
+    model.load_trainable_state_dict(checkpoint["state"])
+    model.eval()
+    for parameter in model.parameters():
+        parameter.requires_grad_(False)
+    head_checkpoint = torch.load(
+        root / "checkpoints/ACTION_HEAD_FINAL.pt",
+        map_location="cpu",
+        weights_only=False,
+    )
+    head = StructuralCopyActionHead(int(head_checkpoint["hidden_size"])).to(device)
+    head.load_state_dict(head_checkpoint["state"], strict=True)
+    head.eval()
+    for parameter in head.parameters():
+        parameter.requires_grad_(False)
+    return config, tokenizer, model, head, codec
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--base-model",
+        default=os.environ.get("STRATA_BASE_MODEL"),
+        help="Local Qwen3-4B-Instruct-2507 snapshot",
+    )
+    parser.add_argument("--event", required=True)
+    parser.add_argument("--predicate", required=True)
+    parser.add_argument("--role", required=True)
+    parser.add_argument("--payload-handle", type=int, required=True)
+    parser.add_argument("--payload", required=True)
+    parser.add_argument("--query", required=True)
+    parser.add_argument("--event-version", type=int, default=1)
+    parser.add_argument("--device", default="cuda:0")
+    args = parser.parse_args()
+    if not args.base_model:
+        parser.error("--base-model or STRATA_BASE_MODEL is required")
+    if not 1 <= args.payload_handle <= 255:
+        parser.error("--payload-handle must be in [1,255]")
+    root = Path(__file__).resolve().parent
+    device = torch.device(args.device)
+    config, tokenizer, model, head, codec = load_model(root, args.base_model, device)
+    row = NativeLMExample(
+        example_id="release-request",
+        split="release",
+        schema=args.event.split(":", 1)[0],
+        field=args.role,
+        event=args.event,
+        predicate=args.predicate,
+        role=args.role,
+        value_type="authoritative",
+        payload_handle=args.payload_handle,
+        value=args.payload,
+        address_codes=address_codes(args.event, args.predicate, args.role),
+        query=args.query,
+        full_history_query=args.query,
+        answer=f"The {args.role.replace('_', ' ')} is {args.payload}.",
+        operation="point",
+        age_windows=0,
+    )
+    authority = PayloadAuthority.issue(
+        event=args.event,
+        predicate=args.predicate,
+        role=args.role,
+        handle=args.payload_handle,
+        payload=args.payload,
+        version=args.event_version,
+    )
+    frame = config["frame"]
+    outputs, timing = frame_separated_generate(
+        model,
+        head,
+        tokenizer,
+        [row],
+        compact_state_table(codec),
+        [[authority]],
+        [0],
+        batch_size=1,
+        max_actions=int(config["evaluation"]["max_actions"]),
+        frame_handle=int(frame["canonical_frame_handle"]),
+        frame_surrogate=str(frame["canonical_frame_surrogate"]),
+        terminator=str(frame["structural_terminator"]),
+        current_versions=[args.event_version],
+    )
+    result = outputs[0]
+    print(
+        json.dumps(
+            {
+                "answer": result.text,
+                "frame": result.frame,
+                "status": result.status,
+                "payload_handle": result.controller_handle,
+                "receipt": authority.receipt,
+                "timing": timing,
+            },
+            ensure_ascii=False,
+            sort_keys=True,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,7 @@
+numpy>=1.24
+pyyaml>=6.0
+scikit-learn>=1.5
+sentencepiece>=0.2.0
+stanza>=1.10,<2
+torch==2.5.1
+transformers==4.53.3

verify.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+"""Verify the SHA-256 manifest for the STRATA Native LM v1 release."""
+
+from __future__ import annotations
+
+import hashlib
+from pathlib import Path
+
+
+def digest(path: Path) -> str:
+    value = hashlib.sha256()
+    with path.open("rb") as handle:
+        for block in iter(lambda: handle.read(1024 * 1024), b""):
+            value.update(block)
+    return value.hexdigest()
+
+
+def main() -> None:
+    root = Path(__file__).resolve().parent
+    manifest = root / "MANIFEST.sha256"
+    failures: list[str] = []
+    entries = 0
+    for line in manifest.read_text(encoding="utf-8").splitlines():
+        if not line:
+            continue
+        expected, relative = line.split("  ", 1)
+        path = root / relative
+        entries += 1
+        if not path.is_file() or digest(path) != expected:
+            failures.append(relative)
+    if failures:
+        raise SystemExit(f"verification failed: {failures}")
+    print(f"VERIFIED {entries} files")
+
+
+if __name__ == "__main__":
+    main()

wheels/strata-0.1.0.post2-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 487641