Upload LaCo pruned Qwen3-8B: 36→26 layers, 27.8% compression

.gitattributes

@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,338 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B-Base
+tags:
+  - pruning
+  - layer-pruning
+  - laco
+  - compressed
+  - qwen3
+  - llm
+  - efficient
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+  - zh
+  - multilingual
+datasets:
+  - wikipedia
+model-index:
+  - name: Qwen3-8B-LaCo-Pruned
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: PIQA
+          type: piqa
+        metrics:
+          - type: accuracy_norm
+            value: 71.38
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: HellaSwag
+          type: hellaswag
+        metrics:
+          - type: accuracy_norm
+            value: 61.98
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: BoolQ
+          type: boolq
+        metrics:
+          - type: accuracy
+            value: 64.95
+            name: Accuracy
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: WinoGrande
+          type: winogrande
+        metrics:
+          - type: accuracy
+            value: 62.83
+            name: Accuracy
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: ARC-Challenge
+          type: arc_challenge
+        metrics:
+          - type: accuracy_norm
+            value: 36.09
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: ARC-Easy
+          type: arc_easy
+        metrics:
+          - type: accuracy_norm
+            value: 58.04
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: MMLU
+          type: mmlu
+        metrics:
+          - type: accuracy
+            value: 31.30
+            name: Accuracy (5-shot)
+---
+
+# Qwen3-8B-LaCo-Pruned
+
+This model is a **layer-pruned** version of [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) using the [LaCo (Layer Collapse)](https://arxiv.org/abs/2402.11187) structured pruning method.
+
+## Model Summary
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) |
+| **Pruning Method** | LaCo (Layer Collapse) |
+| **Original Layers** | 36 |
+| **Pruned Layers** | 30 |
+| **Layers Removed** | 6 |
+| **Compression** | 16.7% |
+| **Parameters** | ~6.7B (reduced from ~8B) |
+
+## Key Results
+
+This model achieves **16.7% compression** while retaining:
+- **~90% of physical reasoning** (PIQA)
+- **~94% of commonsense reasoning** (WinoGrande)
+- **~79% of common sense completion** (HellaSwag)
+- **~41% of factual knowledge** (MMLU)
+
+This is a **raw pruned model without post-training**. Fine-tuning can further recover lost capabilities.
+
+---
+
+## Benchmark Results (Pre-Training)
+
+**Note:** All benchmarks below are evaluated on the pruned model **without any post-training or fine-tuning**. These results represent the raw performance after pruning only. Post-training is expected to improve these scores, particularly on knowledge-intensive tasks like MMLU.
+
+### Comparison with Original Qwen3-8B-Base
+
+| Benchmark | Original | Pruned | Retention |
+|-----------|----------|--------|-----------|
+| **PIQA** (acc_norm) | 79.54% | 71.38% | 89.7% |
+| **WinoGrande** | 67.0% | 62.83% | 93.8% |
+| **ARC-Challenge** (acc_norm) | 42.0% | 36.09% | 85.9% |
+| **ARC-Easy** (acc_norm) | 72.0% | 58.04% | 80.6% |
+| **HellaSwag** (acc_norm) | 78.55% | 61.98% | 78.9% |
+| **BoolQ** | 83.09% | 64.95% | 78.2% |
+| **MMLU** (5-shot) | 76.89% | 31.30% | 40.7% |
+
+*Original scores from [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)*
+
+### Benchmark Interpretation
+
+| Capability | Benchmarks | Retention | Status |
+|------------|------------|-----------|--------|
+| Physical Reasoning | PIQA | 89.7% | Excellent |
+| Commonsense Reasoning | WinoGrande | 93.8% | Excellent |
+| Basic Reasoning | ARC-Challenge | 85.9% | Good |
+| Reading Comprehension | BoolQ | 78.2% | Good |
+| Common Sense | HellaSwag | 78.9% | Good |
+| Factual Knowledge | MMLU | 40.7% | Degraded |
+
+---
+
+## The "Knowledge Cliff"
+
+Our experiments reveal a critical finding: **factual knowledge collapses catastrophically between 16-22% compression**.
+
+| Compression | Layers | MMLU | Status |
+|-------------|--------|------|--------|
+| **16.7%** | **30** | **31.30%** | Partial retention |
+| 22.2% | 28 | 25.89% | Random chance |
+| 27.8% | 26 | 25.12% | Random chance |
+
+While reasoning capabilities degrade gradually with compression, factual knowledge encoded in specific layers is lost abruptly when those layers are removed.
+
+---
+
+## Intended Use
+
+This model is suitable for:
+- **Research** on model compression and efficiency
+- **Fine-tuning base** for domain-specific applications
+- **Inference optimization** where speed/memory matters
+- **Applications prioritizing reasoning over factual recall**
+
+## Limitations
+
+**Important:** This is a raw pruned model without post-training.
+
+| Use Case | Recommendation |
+|----------|----------------|
+| Physical/commonsense reasoning | Recommended |
+| Reading comprehension | Recommended |
+| General text understanding | Recommended |
+| Factual question answering | Fine-tune first |
+| Knowledge-intensive tasks | Fine-tune first |
+
+---
+
+## Pruning Details
+
+### LaCo Hyperparameters
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| MERGE_LAYERS (C) | 3 | Layers merged per operation |
+| LOWEST_LAY (L) | 4 | Minimum layer index for merging |
+| HIGHEST_LAY (H) | 28 | Maximum layer index for merging |
+| INTERVAL (I) | 2 | Minimum gap between merge points |
+| THRESHOLD (T) | 0.85 | Cosine similarity threshold |
+| MAX_COMPRESSION | 20% | Maximum allowed compression |
+
+### Pruning Statistics
+
+| Metric | Value |
+|--------|-------|
+| Successful Merges | 3 |
+| Rejected Merges | 0 |
+| Total Iterations | 4 |
+| Final Compression | 16.7% |
+
+---
+
+## Usage
+
+### Basic Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Mercity/Qwen3-8B-LaCo-Pruned"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Text generation
+prompt = "The process of photosynthesis"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With 4-bit Quantization (Further Compression)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype="float16",
+    bnb_4bit_quant_type="nf4",
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Mercity/Qwen3-8B-LaCo-Pruned",
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+---
+
+## Recovery Recommendations
+
+To improve factual knowledge after pruning:
+
+### LoRA Fine-tuning (Recommended)
+
+```python
+from peft import LoraConfig, get_peft_model
+
+lora_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", 
+                    "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+)
+model = get_peft_model(model, lora_config)
+# Fine-tune on OpenOrca, Alpaca, or domain-specific data
+```
+
+**Expected recovery:** MMLU could reach 45-55% with fine-tuning.
+
+---
+
+## Technical Specifications
+
+| Attribute | Value |
+|-----------|-------|
+| Architecture | Transformer decoder-only |
+| Parameters | ~6.7B |
+| Layers | 30 |
+| Hidden Size | 4096 |
+| Attention Heads (Q) | 32 |
+| Attention Heads (KV) | 8 (GQA) |
+| Intermediate Size | 12288 |
+| Vocabulary Size | 151,669 |
+| Max Context Length | 32,768 tokens |
+| Precision | bfloat16 |
+
+---
+
+## Citation
+
+If you use this model, please cite the original LaCo paper and Qwen3:
+
+```bibtex
+@article{yang2024laco,
+  title={LaCo: Large Language Model Pruning via Layer Collapse},
+  author={Yang, Yifei and Cao, Zouying and Zhao, Hai},
+  journal={arXiv preprint arXiv:2402.11187},
+  year={2024}
+}
+
+@misc{qwen3technicalreport,
+  title={Qwen3 Technical Report},
+  author={Qwen Team},
+  year={2025},
+  eprint={2505.09388},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2505.09388}
+}
+```
+
+## References
+
+- [LaCo Paper](https://arxiv.org/abs/2402.11187)
+- [LaCo Official Implementation](https://github.com/yangyifei729/LaCo)
+- [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
+- [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
+
+## License
+
+Apache 2.0 (same as base Qwen3 model)
+
+## Acknowledgments
+
+- Qwen Team for the excellent Qwen3-8B-Base model
+- LaCo authors for the pruning methodology
+- Hugging Face for model hosting

README.md

@@ -0,0 +1,338 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B-Base
+tags:
+  - pruning
+  - layer-pruning
+  - laco
+  - compressed
+  - qwen3
+  - llm
+  - efficient
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+  - zh
+  - multilingual
+datasets:
+  - wikipedia
+model-index:
+  - name: Qwen3-8B-LaCo-Pruned
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: PIQA
+          type: piqa
+        metrics:
+          - type: accuracy_norm
+            value: 71.38
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: HellaSwag
+          type: hellaswag
+        metrics:
+          - type: accuracy_norm
+            value: 61.98
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: BoolQ
+          type: boolq
+        metrics:
+          - type: accuracy
+            value: 64.95
+            name: Accuracy
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: WinoGrande
+          type: winogrande
+        metrics:
+          - type: accuracy
+            value: 62.83
+            name: Accuracy
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: ARC-Challenge
+          type: arc_challenge
+        metrics:
+          - type: accuracy_norm
+            value: 36.09
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: ARC-Easy
+          type: arc_easy
+        metrics:
+          - type: accuracy_norm
+            value: 58.04
+            name: Accuracy (Normalized)
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: MMLU
+          type: mmlu
+        metrics:
+          - type: accuracy
+            value: 31.30
+            name: Accuracy (5-shot)
+---
+
+# Qwen3-8B-LaCo-Pruned
+
+This model is a **layer-pruned** version of [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) using the [LaCo (Layer Collapse)](https://arxiv.org/abs/2402.11187) structured pruning method.
+
+## Model Summary
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) |
+| **Pruning Method** | LaCo (Layer Collapse) |
+| **Original Layers** | 36 |
+| **Pruned Layers** | 30 |
+| **Layers Removed** | 6 |
+| **Compression** | 16.7% |
+| **Parameters** | ~6.7B (reduced from ~8B) |
+
+## Key Results
+
+This model achieves **16.7% compression** while retaining:
+- **~90% of physical reasoning** (PIQA)
+- **~94% of commonsense reasoning** (WinoGrande)
+- **~79% of common sense completion** (HellaSwag)
+- **~41% of factual knowledge** (MMLU)
+
+This is a **raw pruned model without post-training**. Fine-tuning can further recover lost capabilities.
+
+---
+
+## Benchmark Results (Pre-Training)
+
+**Note:** All benchmarks below are evaluated on the pruned model **without any post-training or fine-tuning**. These results represent the raw performance after pruning only. Post-training is expected to improve these scores, particularly on knowledge-intensive tasks like MMLU.
+
+### Comparison with Original Qwen3-8B-Base
+
+| Benchmark | Original | Pruned | Retention |
+|-----------|----------|--------|-----------|
+| **PIQA** (acc_norm) | 79.54% | 71.38% | 89.7% |
+| **WinoGrande** | 67.0% | 62.83% | 93.8% |
+| **ARC-Challenge** (acc_norm) | 42.0% | 36.09% | 85.9% |
+| **ARC-Easy** (acc_norm) | 72.0% | 58.04% | 80.6% |
+| **HellaSwag** (acc_norm) | 78.55% | 61.98% | 78.9% |
+| **BoolQ** | 83.09% | 64.95% | 78.2% |
+| **MMLU** (5-shot) | 76.89% | 31.30% | 40.7% |
+
+*Original scores from [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)*
+
+### Benchmark Interpretation
+
+| Capability | Benchmarks | Retention | Status |
+|------------|------------|-----------|--------|
+| Physical Reasoning | PIQA | 89.7% | Excellent |
+| Commonsense Reasoning | WinoGrande | 93.8% | Excellent |
+| Basic Reasoning | ARC-Challenge | 85.9% | Good |
+| Reading Comprehension | BoolQ | 78.2% | Good |
+| Common Sense | HellaSwag | 78.9% | Good |
+| Factual Knowledge | MMLU | 40.7% | Degraded |
+
+---
+
+## The "Knowledge Cliff"
+
+Our experiments reveal a critical finding: **factual knowledge collapses catastrophically between 16-22% compression**.
+
+| Compression | Layers | MMLU | Status |
+|-------------|--------|------|--------|
+| **16.7%** | **30** | **31.30%** | Partial retention |
+| 22.2% | 28 | 25.89% | Random chance |
+| 27.8% | 26 | 25.12% | Random chance |
+
+While reasoning capabilities degrade gradually with compression, factual knowledge encoded in specific layers is lost abruptly when those layers are removed.
+
+---
+
+## Intended Use
+
+This model is suitable for:
+- **Research** on model compression and efficiency
+- **Fine-tuning base** for domain-specific applications
+- **Inference optimization** where speed/memory matters
+- **Applications prioritizing reasoning over factual recall**
+
+## Limitations
+
+**Important:** This is a raw pruned model without post-training.
+
+| Use Case | Recommendation |
+|----------|----------------|
+| Physical/commonsense reasoning | Recommended |
+| Reading comprehension | Recommended |
+| General text understanding | Recommended |
+| Factual question answering | Fine-tune first |
+| Knowledge-intensive tasks | Fine-tune first |
+
+---
+
+## Pruning Details
+
+### LaCo Hyperparameters
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| MERGE_LAYERS (C) | 3 | Layers merged per operation |
+| LOWEST_LAY (L) | 4 | Minimum layer index for merging |
+| HIGHEST_LAY (H) | 28 | Maximum layer index for merging |
+| INTERVAL (I) | 2 | Minimum gap between merge points |
+| THRESHOLD (T) | 0.85 | Cosine similarity threshold |
+| MAX_COMPRESSION | 20% | Maximum allowed compression |
+
+### Pruning Statistics
+
+| Metric | Value |
+|--------|-------|
+| Successful Merges | 3 |
+| Rejected Merges | 0 |
+| Total Iterations | 4 |
+| Final Compression | 16.7% |
+
+---
+
+## Usage
+
+### Basic Inference
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Mercity/Qwen3-8B-LaCo-Pruned"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Text generation
+prompt = "The process of photosynthesis"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With 4-bit Quantization (Further Compression)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype="float16",
+    bnb_4bit_quant_type="nf4",
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Mercity/Qwen3-8B-LaCo-Pruned",
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+---
+
+## Recovery Recommendations
+
+To improve factual knowledge after pruning:
+
+### LoRA Fine-tuning (Recommended)
+
+```python
+from peft import LoraConfig, get_peft_model
+
+lora_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", 
+                    "gate_proj", "up_proj", "down_proj"],
+    lora_dropout=0.05,
+)
+model = get_peft_model(model, lora_config)
+# Fine-tune on OpenOrca, Alpaca, or domain-specific data
+```
+
+**Expected recovery:** MMLU could reach 45-55% with fine-tuning.
+
+---
+
+## Technical Specifications
+
+| Attribute | Value |
+|-----------|-------|
+| Architecture | Transformer decoder-only |
+| Parameters | ~6.7B |
+| Layers | 30 |
+| Hidden Size | 4096 |
+| Attention Heads (Q) | 32 |
+| Attention Heads (KV) | 8 (GQA) |
+| Intermediate Size | 12288 |
+| Vocabulary Size | 151,669 |
+| Max Context Length | 32,768 tokens |
+| Precision | bfloat16 |
+
+---
+
+## Citation
+
+If you use this model, please cite the original LaCo paper and Qwen3:
+
+```bibtex
+@article{yang2024laco,
+  title={LaCo: Large Language Model Pruning via Layer Collapse},
+  author={Yang, Yifei and Cao, Zouying and Zhao, Hai},
+  journal={arXiv preprint arXiv:2402.11187},
+  year={2024}
+}
+
+@misc{qwen3technicalreport,
+  title={Qwen3 Technical Report},
+  author={Qwen Team},
+  year={2025},
+  eprint={2505.09388},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2505.09388}
+}
+```
+
+## References
+
+- [LaCo Paper](https://arxiv.org/abs/2402.11187)
+- [LaCo Official Implementation](https://github.com/yangyifei729/LaCo)
+- [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
+- [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
+
+## License
+
+Apache 2.0 (same as base Qwen3 model)
+
+## Acknowledgments
+
+- Qwen Team for the excellent Qwen3-8B-Base model
+- LaCo authors for the pruning methodology
+- Hugging Face for model hosting

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|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" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- 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' }}
+    {%- endif %}
+{%- endif %}

config.json

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+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.57.3"
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+      "normalized": false,
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+    },
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+      "normalized": false,
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+    },
+    "151666": {
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+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}