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Model: youngzhong/SOD-0.6B Source: Original Platform
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101
README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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base_model: Qwen/Qwen3-0.6B
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tags:
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- agent
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- tool-use
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- distillation
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- math
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- code
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- reasoning
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pipeline_tag: text-generation
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---
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<div align="center">
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<h1>SOD-0.6B</h1>
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<p>
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<a href="https://arxiv.org/abs/2605.07725">
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<img src="https://img.shields.io/badge/Paper-arXiv-red?logo=arxiv&logoColor=red" alt="Paper on arXiv"/>
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</a>
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<a href="https://github.com/YoungZ365/SOD">
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<img src="https://img.shields.io/badge/Code-GitHub-black?logo=github&logoColor=white" alt="Code on GitHub"/>
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</a>
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<a href="https://huggingface.co/collections/youngzhong/sod-6a03530369d76913c24a4ffb">
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<img src="https://img.shields.io/badge/Collection-SOD-yellow?logo=huggingface" alt="HuggingFace Collection"/>
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</a>
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<a href="https://huggingface.co/papers/2605.07725">
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<img src="https://img.shields.io/badge/Daily%20Paper-SOD-yellow?logo=huggingface&logoColor=yellow" alt="HuggingFace Daily Paper"/>
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</a>
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<a href="https://www.alphaxiv.org/abs/2605.07725">
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<img src="https://img.shields.io/badge/alphaXiv-2605.07725-purple?logo=arxiv&logoColor=white" alt="alphaXiv"/>
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</a>
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</p>
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</div>
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## About
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**SOD-0.6B** is a 0.6B student model distilled from a 4B teacher using **SOD (Step-wise On-policy Distillation)**, a method designed for training small language model agents with tool-integrated reasoning capabilities.
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SOD addresses the **cascading error propagation** problem in on-policy distillation for agentic reasoning by introducing an adaptive step-level weighting mechanism that suppresses distillation loss on drifted steps and restores supervision when the student recovers alignment — all at negligible additional computational cost.
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## Model Information
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| Attribute | Value |
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|-----------|-------|
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| Base Model | [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
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| Teacher Model | [SOD-GRPO_teacher-4B](https://huggingface.co/youngzhong/SOD-GRPO_teacher-4B) |
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| Training Pipeline | Cold-Start SFT → SOD (Step-wise On-policy Distillation) |
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| Parameters | 0.6B |
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## Related Models
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| Model | Description |
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|-------|-------------|
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| [SOD-0.6B](https://huggingface.co/youngzhong/SOD-0.6B) | SOD-distilled 0.6B student (this model) |
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| [SOD-1.7B](https://huggingface.co/youngzhong/SOD-1.7B) | SOD-distilled 1.7B student |
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| [SOD-GRPO_teacher-4B](https://huggingface.co/youngzhong/SOD-GRPO_teacher-4B) | GRPO-trained 4B teacher model |
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## Performance
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We report **average@32** over 5 runs on challenging math, science, and code benchmarks.
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### 0.6B Student Results
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| Method | AIME 2024 | AIME 2025 | GPQA-Diamond | LiveCodeBench-v6 | Average |
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|--------|-----------|-----------|--------------|------------------|---------|
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| Vanilla | 7.71 | 12.81 | 13.24 | 14.89 | 12.16 |
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| SFT | 5.67 | 5.42 | 15.20 | 9.61 | 8.97 |
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| GRPO | 4.06 | 4.90 | 20.38 | 15.95 | 11.32 |
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| OPD | 16.82 | 22.95 | 17.76 | 22.65 | 20.04 |
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| OPSD_gt | 12.63 | 17.04 | 17.32 | 16.73 | 15.93 |
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| OPSD_hint | 9.77 | 14.12 | 15.98 | 12.65 | 13.13 |
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| **SOD (This Model)** | **20.84** | **26.13** | **22.19** | **27.72** | **24.22** |
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### Teacher Model (4B)
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| Method | AIME 2024 | AIME 2025 | GPQA-Diamond | LiveCodeBench-v6 | Average |
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|--------|-----------|-----------|--------------|------------------|---------|
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| GRPO | 67.60 | 60.42 | 55.19 | 63.13 | 61.59 |
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## Key Highlights
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- 🏆 **Strong 0.6B agent**: Achieves **26.13%** on AIME 2025 (average@32)
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- 📈 **+20.86% over second-best baseline** (OPD) on average
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- 💡 **Minimal extra compute**: The divergence metric reuses log-probabilities already computed in the forward pass
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## Citation
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```bibtex
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@article{zhong2026sod,
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title={SOD: Step-wise On-policy Distillation for Small Language Model Agents},
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author={Zhong, Qiyong and Zheng, Mao and Song, Mingyang and Lin, Xin and Sun, Jie and Jiang, Houcheng and Wang, Xiang and Fang, Junfeng},
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journal={arXiv preprint arXiv:2605.07725},
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year={2026}
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}
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```
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89
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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63
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.4.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.4.0"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e56493ef5f2b52bd568c2e48866928d0a3a166e3fd48621cd13a8c30819bfe0
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size 1503300328
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
|
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"<|im_start|>",
|
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"<|im_end|>",
|
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"<|object_ref_start|>",
|
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"<|object_ref_end|>",
|
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"<|box_start|>",
|
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
|
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"<|vision_start|>",
|
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"<|vision_end|>",
|
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"<|vision_pad|>",
|
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"<|image_pad|>",
|
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"<|video_pad|>"
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],
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"is_local": true,
|
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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