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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 shibatch
+
+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.

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+license: mit
+datasets:
+  - roneneldan/TinyStories
+tags:
+  - qwen3-next
+  - gated-deltanet
+  - hybrid-attention
+  - mixture-of-experts
+  - moe
+  - tinystories
+  - tiny-model
+  - text-generation
+  - validation
+  - debug-model
+---
+
+# Tiny Qwen3-Next 3M
+
+This repository contains a tiny `Qwen3NextForCausalLM` Mixture-of-Experts
+language model trained from scratch on TinyStories.
+
+The model has **2,945,914 parameters**. It combines Gated DeltaNet linear
+attention, gated full attention, sparse top-2 MoE routing, and a shared expert
+in a checkpoint small enough for implementation testing and experimentation.
+
+This is a synthetic tiny checkpoint. It is not an official Qwen model, does
+not contain weights from an original Qwen checkpoint, and should not be
+expected to match the quality or capabilities of production Qwen models.
+
+## Repository contents
+
+- `hf/`: the final Hugging Face checkpoint and tokenizer
+- `example_generate.py`: a minimal generation example
+- `eval_text_generation.json`: generations from the final training evaluation
+- `artifact_metadata.json`: training arguments, metrics, router usage, and the
+  expanded configuration
+- `qwen3_next_config_dump.json`: a standalone configuration dump
+
+Optimizer checkpoints and the full training log are intentionally omitted
+from the distribution package.
+
+## Important architecture distinction
+
+This checkpoint uses:
+
+```text
+Qwen3NextConfig
+Qwen3NextForCausalLM
+model_type: qwen3_next
+```
+
+It does **not** use the separate regular Qwen3 MoE implementation:
+
+```text
+Qwen3MoeConfig
+Qwen3MoeForCausalLM
+model_type: qwen3_moe
+```
+
+The model follows the Qwen3-Next hybrid layer pattern: three Gated DeltaNet
+linear-attention layers followed by one gated full-attention layer. Every
+layer contains a sparse MoE block.
+
+## Relationship to Qwen3-Next 80B
+
+The architecture is a deliberately scaled-down member of the same
+Transformers architecture family as `Qwen/Qwen3-Next-80B-A3B-Instruct`:
+
+```text
+3 x (Gated DeltaNet -> MoE)
+1 x (Gated full attention -> MoE)
+```
+
+The original model repeats this four-layer pattern multiple times and uses
+hundreds of experts. Tiny Qwen3-Next 3M keeps one four-layer cycle and eight
+experts so that the important execution paths remain present at a much
+smaller scale.
+
+This checkpoint is pretrained only. It has not been instruction-tuned and
+does not reproduce Qwen3-Next 80B behavior.
+
+## Model architecture
+
+```yaml
+architecture: Qwen3NextForCausalLM
+model_type: qwen3_next
+parameter_count: 2,945,914
+
+model_vocab_size: 1,024
+tokenizer_size: 1,003
+hidden_size: 216
+intermediate_size: 540
+num_hidden_layers: 4
+
+layer_types:
+  - linear_attention
+  - linear_attention
+  - linear_attention
+  - full_attention
+
+num_attention_heads: 8
+num_key_value_heads: 1
+head_dim: 56
+partial_rotary_factor: 0.25
+rope_theta: 10,000,000
+
+linear_num_key_heads: 4
+linear_key_head_dim: 54
+linear_num_value_heads: 8
+linear_value_head_dim: 54
+linear_conv_kernel_dim: 4
+
+num_experts: 8
+num_experts_per_tok: 2
+moe_intermediate_size: 54
+shared_expert_intermediate_size: 54
+norm_topk_prob: true
+router_aux_loss_coef: 0.01
+
+tie_word_embeddings: true
+rms_norm_eps: 1.0e-6
+max_position_embeddings: 1,024
+```
+
+The Gated DeltaNet dimensions retain the characteristic Qwen3-Next ratios:
+
+- key width: `4 x 54 = 216`, equal to the hidden size
+- value width: `8 x 54 = 432`, twice the hidden size
+
+Full attention uses eight query heads and one key/value head. The query width
+is `8 x 56 = 448`, while the key/value width is 56.
+
+## MoE configuration
+
+Each of the four decoder layers contains eight routed experts and one shared
+expert. Every token selects two of the eight routed experts:
+
+```yaml
+routed_experts: 8
+top_k: 2
+shared_expert: true
+normalized_top_k_weights: true
+```
+
+Router auxiliary loss was active during training. All experts received
+traffic. Final aggregate expert fractions ranged from approximately 0.060 to
+0.208 depending on layer and expert; no expert was unused.
+
+## Training data
+
+The model was trained on the full TinyStories training corpus using an
+independent 1% validation split:
+
+```yaml
+selected_stories: 2,119,489
+training_stories: 2,098,294
+validation_stories: 21,195
+validation_fraction: 0.01
+
+training_blocks: 2,441,089
+validation_blocks: 24,608
+block_size: 256
+```
+
+Stories were joined into a continuous packed stream:
+
+```text
+BOS + story 1 + EOS + BOS + story 2 + EOS + ...
+```
+
+The stream was split into fixed 256-token blocks without padding. Only the
+final incomplete block was discarded.
+
+## Tokenizer
+
+The checkpoint uses a small custom byte-level BPE tokenizer. The base BPE
+vocabulary was trained with:
+
+```text
+BPE()
+ByteLevel(add_prefix_space=False)
+base_vocab_size: 1,000
+min_frequency: 2
+normalizer: None
+```
+
+Special tokens were then added at fixed IDs:
+
+```text
+<s>          -> 1000
+</s>         -> 1001
+<|im_start|> -> 1002
+```
+
+The tokenizer uses `<s>` as both BOS and padding, and `</s>` as EOS. The model
+configuration reserves 1,024 embedding rows while the tokenizer exposes
+1,003 tokens.
+
+## Training setup
+
+The checkpoint was trained from scratch in float32 on an NVIDIA GeForce GTX
+1650:
+
+```yaml
+dtype: float32
+batch_size: 16
+block_size: 256
+training_steps: 152,568
+epochs: 1.0
+tokens_processed: 624,918,528
+
+optimizer: AdamW
+learning_rate: 3.0e-4
+warmup_steps: 1,000
+scheduler: warmup + cosine decay
+minimum_learning_rate: 3.0e-5
+weight_decay: 0.0
+grad_clip: 1.0
+
+training_time: 13h 48m 45s
+```
+
+## Evaluation
+
+The final checkpoint produced:
+
+```yaml
+final_train_loss: 1.4145
+validation_loss: 1.4320
+validation_perplexity: 4.1871
+```
+
+Validation loss was computed over 16 batches, or 65,536 tokens, from the
+independent packed validation split. These numbers are compact checkpoint
+diagnostics, not general language-model benchmark results.
+
+## Example generation
+
+With prompt `Once upon`, sampling seed 0 produced the following representative
+TinyStories-style opening:
+
+```text
+Once upon a time, there was a little girl named Lily. She loved to play with
+her toys and go on adventures. One day, she went outside and saw a rainbow in
+the sky. It was so pretty that it made her feel even happier than before.
+```
+
+Sampling is stochastic. Other seeds may produce a boy, an adult, an animal,
+or another kind of TinyStories character. The model usually produces
+recognizable English, but semantic contradictions, unfinished sentences,
+invented words, and repetition remain possible at this size.
+
+## Usage
+
+Install the requirements:
+
+```bash
+pip install -r requirements.txt
+```
+
+Run the included local example from the repository root:
+
+```bash
+python example_generate.py
+```
+
+To load the package from Hugging Face Hub, resolve the `hf` directory to a
+local path first. This also avoids a Transformers 5.14.1 local-subfolder issue
+in which generation configuration lookup may incorrectly fall back to the
+repository root:
+
+```python
+import torch
+from pathlib import Path
+from huggingface_hub import snapshot_download
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "shibatch/tinyqwen3next3m"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_dir = Path(snapshot_download(
+    repo_id=repo,
+    allow_patterns=["hf/*"],
+)) / "hf"
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForCausalLM.from_pretrained(
+    model_dir,
+    dtype=torch.float32,
+).to(device)
+model.eval()
+
+prompt = "Once upon"
+input_ids = torch.tensor(
+    [[tokenizer.bos_token_id] + tokenizer.encode(
+        prompt,
+        add_special_tokens=False,
+    )],
+    dtype=torch.long,
+    device=device,
+)
+
+torch.manual_seed(0)
+if device.type == "cuda":
+    torch.cuda.manual_seed_all(0)
+
+with torch.no_grad():
+    output = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=100,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.95,
+        top_k=40,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        output_router_logits=False,
+    )
+
+print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+```
+
+## Loading requirements
+
+The checkpoint requires a Transformers release containing
+`Qwen3NextForCausalLM`. It was trained and tested with:
+
+```text
+transformers 5.14.1
+torch 2.14.0.dev20260720+cu126
+```
+
+The optional `flash-linear-attention` and `causal-conv1d` packages are not
+required. Without them, Transformers uses its PyTorch Gated DeltaNet fallback,
+which is the path used to train and validate this checkpoint.
+
+## Intended uses
+
+This model is intended for:
+
+- testing `Qwen3NextConfig` and `Qwen3NextForCausalLM`
+- testing Gated DeltaNet fallback implementations
+- testing the hybrid linear/full-attention layer pattern
+- testing sparse top-2 MoE routing and shared experts
+- checking router load-balancing loss
+- exercising custom tokenizer loading
+- testing `generate()`, `save_pretrained()`, and `from_pretrained()`
+- compact inference-engine and architecture experiments
+
+It is not intended for:
+
+- instruction following or chat
+- factual question answering
+- high-quality long-form generation
+- production deployment
+- safety-critical use
+- benchmark comparison with production Qwen models
+
+## Limitations
+
+Known limitations include:
+
+- only 2.95 million parameters
+- small 1,003-token tokenizer
+- English TinyStories-only pretraining
+- weak factual knowledge and reasoning
+- no instruction tuning or chat template
+- occasional grammatical and semantic errors
+- invented words and truncated sentences
+- repetition and template-like stories
+- no quality-equivalence claim with official Qwen models
+- no current llama.cpp or GGUF inference support assumed for Qwen3-Next
+
+## Notes on GGUF
+
+The checkpoint is distributed as a normal float32 Hugging Face Safetensors
+model. A useful GGUF build requires converter and runtime support for the full
+Qwen3-Next graph, including Gated DeltaNet, hybrid attention, routed experts,
+and the shared expert. Merely placing tensors in a GGUF container is not
+sufficient for compatible inference.
+
+## Citation
+
+This is a synthetic tiny Qwen3-Next-compatible MoE checkpoint trained from
+scratch on TinyStories. It is intended for implementation validation,
+debugging, education, and small-scale architecture experiments.

artifact_metadata.json

@@ -0,0 +1,205 @@
+{
+  "model_type": "qwen3_gated_moe_qwen3_next",
+  "hf_model_type": "qwen3_next",
+  "architecture": "qwen3_next",
+  "parameter_count": 2945914,
+  "tokenizer_size": 1003,
+  "final_eval_loss": 1.4319972917437553,
+  "final_eval_ppl": 4.187053613650411,
+  "final_train_lm_loss": 1.4144856333732605,
+  "router_usage": {
+    "model.layers.0.mlp.gate": [
+      0.1257,
+      0.098,
+      0.1444,
+      0.1294,
+      0.157,
+      0.1163,
+      0.1017,
+      0.1274
+    ],
+    "model.layers.1.mlp.gate": [
+      0.1737,
+      0.0915,
+      0.0998,
+      0.1762,
+      0.1692,
+      0.0784,
+      0.0892,
+      0.1219
+    ],
+    "model.layers.2.mlp.gate": [
+      0.1078,
+      0.1311,
+      0.2018,
+      0.091,
+      0.1046,
+      0.1521,
+      0.1415,
+      0.07
+    ],
+    "model.layers.3.mlp.gate": [
+      0.0827,
+      0.1803,
+      0.0596,
+      0.1023,
+      0.066,
+      0.1445,
+      0.1774,
+      0.1873
+    ]
+  },
+  "args": {
+    "train_files": [
+      "data/train-00000-of-00004-2d5a1467fff1081b.parquet",
+      "data/train-00001-of-00004-5852b56a2bd28fd9.parquet",
+      "data/train-00002-of-00004-a26307300439e943.parquet",
+      "data/train-00003-of-00004-d243063613e5a057.parquet"
+    ],
+    "output_dir": "runs/qwen3_next_3m_fix2_100p_1epoch",
+    "max_rows": 0,
+    "data_fraction": 1.0,
+    "vocab_size": 1024,
+    "base_vocab_size": 1000,
+    "min_frequency": 2,
+    "legacy_tokenizer": true,
+    "legacy_special_token_ids": true,
+    "architecture": "qwen3_next",
+    "hidden_size": 216,
+    "intermediate_size": 540,
+    "moe_intermediate_size": 54,
+    "shared_expert_intermediate_size": 54,
+    "num_hidden_layers": 4,
+    "num_attention_heads": 8,
+    "num_key_value_heads": 1,
+    "head_dim": 56,
+    "max_position_embeddings": 1024,
+    "attention_bias": false,
+    "freeze_random_qkv_bias": false,
+    "qkv_bias_init_range": 0.2,
+    "attention_dropout": 0.0,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 10000000.0,
+    "partial_rotary_factor": 0.25,
+    "use_cache": false,
+    "tie_word_embeddings": true,
+    "decoder_sparse_step": 1,
+    "mlp_only_layers": "",
+    "num_local_experts": 8,
+    "num_experts_per_tok": 2,
+    "norm_topk_prob": true,
+    "output_router_logits": true,
+    "router_aux_loss_coef": 0.01,
+    "layer_types": "",
+    "full_attention_interval": 4,
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 54,
+    "linear_value_head_dim": 54,
+    "linear_num_key_heads": 4,
+    "linear_num_value_heads": 8,
+    "block_size": 256,
+    "batch_size": 16,
+    "max_steps": 152568,
+    "num_epochs": 1.0,
+    "learning_rate": 0.0003,
+    "weight_decay": 0.0,
+    "grad_clip": 1.0,
+    "warmup_steps": 1000,
+    "min_lr_ratio": 0.1,
+    "validation_fraction": 0.01,
+    "log_steps": 100,
+    "eval_steps": 5000,
+    "eval_batches": 16,
+    "save_steps": 10000,
+    "resume_from_checkpoint": "",
+    "seed": 1234,
+    "device": "cuda",
+    "dtype": "float32",
+    "num_workers": 0,
+    "generate_new_tokens": 100,
+    "generate_prompts": [
+      "Once upon",
+      "There was a little",
+      "One day"
+    ],
+    "generation_temperature": 0.8,
+    "generation_top_p": 0.95,
+    "generation_top_k": 40,
+    "generation_repetition_penalty": 1.1,
+    "do_sample": true
+  },
+  "config": {
+    "transformers_version": "5.14.1",
+    "architectures": [
+      "Qwen3NextForCausalLM"
+    ],
+    "output_hidden_states": false,
+    "return_dict": true,
+    "dtype": "float32",
+    "chunk_size_feed_forward": 0,
+    "is_encoder_decoder": false,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "problem_type": null,
+    "vocab_size": 1024,
+    "hidden_size": 216,
+    "intermediate_size": 540,
+    "num_hidden_layers": 4,
+    "num_attention_heads": 8,
+    "num_key_value_heads": 1,
+    "hidden_act": "silu",
+    "max_position_embeddings": 1024,
+    "initializer_range": 0.02,
+    "rms_norm_eps": 1e-06,
+    "use_cache": false,
+    "tie_word_embeddings": true,
+    "rope_parameters": {
+      "rope_type": "default",
+      "rope_theta": 10000000.0,
+      "partial_rotary_factor": 0.25
+    },
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "head_dim": 56,
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 54,
+    "linear_value_head_dim": 54,
+    "linear_num_key_heads": 4,
+    "linear_num_value_heads": 8,
+    "decoder_sparse_step": 1,
+    "moe_intermediate_size": 54,
+    "shared_expert_intermediate_size": 54,
+    "num_experts_per_tok": 2,
+    "num_experts": 8,
+    "norm_topk_prob": true,
+    "output_router_logits": true,
+    "router_aux_loss_coef": 0.01,
+    "mlp_only_layers": [],
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "pad_token_id": 1000,
+    "bos_token_id": 1000,
+    "eos_token_id": 1001,
+    "_name_or_path": "",
+    "use_sliding_window": false,
+    "sliding_window": null,
+    "partial_rotary_factor": 0.25,
+    "model_type": "qwen3_next",
+    "output_attentions": false
+  },
+  "generations": {
+    "Once upon": "Once upon a time, there was a mighty man. He liked to count things and shout out loud. One day he decided to go on an adventure in the forest. \n\nHe went into a dark forest and saw many trees that were all around him. The first man was very excited. He wanted to find someone to give himself something special. So he ran off to find someone who could help him. \n\nThe s",
+    "There was a little": "There was a little girl named Lucy who lived in a small house. One day, she heard a whistle coming from the ceiling. She was so excited and wanted to see what it could do.\n\nLucy went up to the sound and suddenly she saw a big tree. It had lots of junk inside! The tree looked like it was going to perform for her friend and her friend. They climbed the tree together and L",
+    "One day": "One day, a boy named Tim went for a walk. He saw his friend, Sam, who was also tall and strong.\n\n\"Hi Sam!\" said Tim. \"I want to be my friend.\"\n\nSam looked at Tim. \"You can share the ball with me,\" he said. \"Thank you for sharing the ball with me.\"\n\nTim and Sam decided to share the ball. They took turns pulling and sharing"
+  }
+}

eval_text_generation.json

@@ -0,0 +1,5 @@
+{
+  "Once upon": "Once upon a time, there was a mighty man. He liked to count things and shout out loud. One day he decided to go on an adventure in the forest. \n\nHe went into a dark forest and saw many trees that were all around him. The first man was very excited. He wanted to find someone to give himself something special. So he ran off to find someone who could help him. \n\nThe s",
+  "There was a little": "There was a little girl named Lucy who lived in a small house. One day, she heard a whistle coming from the ceiling. She was so excited and wanted to see what it could do.\n\nLucy went up to the sound and suddenly she saw a big tree. It had lots of junk inside! The tree looked like it was going to perform for her friend and her friend. They climbed the tree together and L",
+  "One day": "One day, a boy named Tim went for a walk. He saw his friend, Sam, who was also tall and strong.\n\n\"Hi Sam!\" said Tim. \"I want to be my friend.\"\n\nSam looked at Tim. \"You can share the ball with me,\" he said. \"Thank you for sharing the ball with me.\"\n\nTim and Sam decided to share the ball. They took turns pulling and sharing"
+}

example_generate.py

@@ -0,0 +1,54 @@
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+MODEL_PATH = "."
+MODEL_SUBFOLDER = "hf"
+PROMPT = "Once upon"
+SEED = 0
+
+
+def main() -> None:
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model_dir = Path(MODEL_PATH) / MODEL_SUBFOLDER
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_dir,
+        dtype=torch.float32,
+    ).to(device)
+    model.eval()
+
+    input_ids = torch.tensor(
+        [
+            [tokenizer.bos_token_id]
+            + tokenizer.encode(PROMPT, add_special_tokens=False)
+        ],
+        dtype=torch.long,
+        device=device,
+    )
+
+    torch.manual_seed(SEED)
+    if device.type == "cuda":
+        torch.cuda.manual_seed_all(SEED)
+
+    with torch.no_grad():
+        output = model.generate(
+            input_ids=input_ids,
+            max_new_tokens=100,
+            do_sample=True,
+            temperature=0.8,
+            top_p=0.95,
+            top_k=40,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            output_router_logits=False,
+        )
+
+    print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+
+
+if __name__ == "__main__":
+    main()

hf/config.json

@@ -0,0 +1,54 @@
+{
+  "architectures": [
+    "Qwen3NextForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1000,
+  "decoder_sparse_step": 1,
+  "dtype": "float32",
+  "eos_token_id": 1001,
+  "head_dim": 56,
+  "hidden_act": "silu",
+  "hidden_size": 216,
+  "initializer_range": 0.02,
+  "intermediate_size": 540,
+  "layer_types": [
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention"
+  ],
+  "linear_conv_kernel_dim": 4,
+  "linear_key_head_dim": 54,
+  "linear_num_key_heads": 4,
+  "linear_num_value_heads": 8,
+  "linear_value_head_dim": 54,
+  "max_position_embeddings": 1024,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_next",
+  "moe_intermediate_size": 54,
+  "norm_topk_prob": true,
+  "num_attention_heads": 8,
+  "num_experts": 8,
+  "num_experts_per_tok": 2,
+  "num_hidden_layers": 4,
+  "num_key_value_heads": 1,
+  "output_router_logits": true,
+  "pad_token_id": 1000,
+  "partial_rotary_factor": 0.25,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "partial_rotary_factor": 0.25,
+    "rope_theta": 10000000.0,
+    "rope_type": "default"
+  },
+  "router_aux_loss_coef": 0.01,
+  "shared_expert_intermediate_size": 54,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 1024
+}

hf/generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1000,
+  "eos_token_id": 1001,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 1000,
+  "transformers_version": "5.14.1",
+  "use_cache": false
+}

hf/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:572f9a3c08c7411ff12ebc5f8512af9d6eef613b33b11c2fab69ab7e67e8ac4a
+size 11800776

hf/tokenizer.json

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+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 1000,
+      "content": "<s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1001,
+      "content": "</s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1002,
+      "content": "<|im_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "ByteLevel",
+    "add_prefix_space": false,
+    "trim_offsets": false,
+    "use_regex": true
+  },
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      }
+    ],
+    "pair": [
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "B",
+          "type_id": 1
+        }
+      }
+    ],
+    "special_tokens": {}
+  },
+  "decoder": {
+    "type": "ByteLevel",
+    "add_prefix_space": true,
+    "trim_offsets": true,
+    "use_regex": true
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+        "T",
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+        "Ġf",
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+      [
+        "Ġwa",
+        "r"
+      ]
+    ]
+  }
+}

hf/tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": [
+    "<|im_start|>"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<s>",
+  "tokenizer_class": "TokenizersBackend"
+}

qwen3_next_config_dump.json

@@ -0,0 +1,67 @@
+{
+  "transformers_version": "5.14.1",
+  "architectures": null,
+  "output_hidden_states": false,
+  "return_dict": true,
+  "dtype": null,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "problem_type": null,
+  "vocab_size": 1024,
+  "hidden_size": 216,
+  "intermediate_size": 540,
+  "num_hidden_layers": 4,
+  "num_attention_heads": 8,
+  "num_key_value_heads": 1,
+  "hidden_act": "silu",
+  "max_position_embeddings": 1024,
+  "initializer_range": 0.02,
+  "rms_norm_eps": 1e-06,
+  "use_cache": false,
+  "tie_word_embeddings": true,
+  "rope_parameters": {
+    "rope_type": "default",
+    "rope_theta": 10000000.0,
+    "partial_rotary_factor": 0.25
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "head_dim": 56,
+  "linear_conv_kernel_dim": 4,
+  "linear_key_head_dim": 54,
+  "linear_value_head_dim": 54,
+  "linear_num_key_heads": 4,
+  "linear_num_value_heads": 8,
+  "decoder_sparse_step": 1,
+  "moe_intermediate_size": 54,
+  "shared_expert_intermediate_size": 54,
+  "num_experts_per_tok": 2,
+  "num_experts": 8,
+  "norm_topk_prob": true,
+  "output_router_logits": true,
+  "router_aux_loss_coef": 0.01,
+  "mlp_only_layers": [],
+  "layer_types": [
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention"
+  ],
+  "pad_token_id": 1000,
+  "bos_token_id": 1000,
+  "eos_token_id": 1001,
+  "_name_or_path": "",
+  "use_sliding_window": false,
+  "sliding_window": null,
+  "partial_rotary_factor": 0.25,
+  "model_type": "qwen3_next",
+  "output_attentions": false
+}

requirements.txt

@@ -0,0 +1,3 @@
+torch
+transformers>=5.14.1
+huggingface_hub