commit edfa8c1ba3387567fbfc3b1b9c4209bc78876082 Author: ModelHub XC Date: Fri May 22 02:40:15 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Jackrong/Llama3.1-8B-Thinking-R1 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz 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 diff --git a/README.md b/README.md new file mode 100644 index 0000000..267bdf2 --- /dev/null +++ b/README.md @@ -0,0 +1,66 @@ +--- +license: llama3.1 +base_model: unsloth/Llama-3.1-8B-Instruct +tags: +- reasoning +- thinking +- grpo +- r1 +- llama-cpp +- gguf +datasets: +- unsloth/OpenMathReasoning-mini +- open-r1/DAPO-Math-17k-Processed +- Jackrong/ShareGPT-gpt-oss-120B-reasoning +- Jackrong/Chinese-Qwen3-235B-Thinking-Distill +- Jackrong/MultiReason-ChatAlpaca +language: +- en +- zh +pipeline_tag: text-generation +--- + +# Llama3.1-8B-Thinking-R1 + + +![Gemini_Generated_Image_uahqqguahqqguahq](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/yh7CCx2VuHj7CAUd0Oq3K.png) + +## 1. Model Summary +**Jackrong/Llama3.1-8B-Thinking-R1** is a deep reasoning model built upon `Llama-3.1-8B-Instruct`. This model is designed to solve complex logic, mathematics, and programming problems through a structured "Think-and-Answer" paradigm. + +The core feature of the model is its refined Chain-of-Thought (CoT) capability. Before providing a final answer, the model performs self-correction, logical decomposition, and multi-path exploration within `` tags. + +## 2. Training Methodology +This model utilizes a unique three-stage training pipeline to ensure stability and depth in reasoning: + +### Stage 1: Cold-start SFT (Supervised Fine-Tuning) +Initial fine-tuning is performed using high-quality mathematical reasoning data to help the model acquire basic reasoning formats. During this stage, the model learns how to use `` tags for logical guidance and establishes its initial mental framework. + +### Stage 2: GRPO Reinforcement Learning (Group Relative Policy Optimization) +The **GRPO** algorithm is employed to conduct large-scale reinforcement training, guided by **Accuracy Rewards** and **Format Rewards**. In this phase, the model not only learns how to reach the correct answer but also optimizes the efficiency of its thought process, reducing logical redundancy. + +### Stage 3: Final CoT Distillation SFT +Building upon the reinforcement learning stage, the model undergoes final instruction fine-tuning using high-quality CoT data distilled from ultra-large-scale models (such as GPT-OSS-120B and Qwen3-235B). This stage significantly enhances the model's expressiveness in complex contexts and improves logical rigor. + +## 3. Training Features +- **Reinforcement Learning Framework**: Utilizes the **GRPO** algorithm, guiding the model to autonomously learn logical decomposition via format and accuracy rewards. +- **Cold-start SFT**: Uses datasets like `OpenMathReasoning` for warm-up, ensuring the model masters the fundamental thinking format. +- **Multi-stage Distillation**: Incorporates reasoning logic distilled from 120B+ scale models, significantly boosting Chinese logic and multi-turn dialogue reasoning performance. +- **Efficient Fine-Tuning**: Built on the **Unsloth** framework using LoRA (Rank 64) technology to maintain reasoning capabilities while mitigating catastrophic forgetting. +- **Long Context Support**: Supports a context length of up to **65,536** tokens, capable of handling complex, long-chain reasoning tasks. + +## 4. Datasets +The model evolved through the three stages mentioned above using a combination of the following datasets: + +- **unsloth/OpenMathReasoning-mini**: Provides core mathematical reasoning logic. +- **open-r1/DAPO-Math-17k-Processed**: Used for alignment optimization during the RL phase. +- **Jackrong/ShareGPT-gpt-oss-120B-reasoning**: Introduces English reasoning path distillation from ultra-large models. +- **Jackrong/Chinese-Qwen3-235B-Thinking-Distill**: Specifically enhances the depth of Chinese logical thinking. +- **Jackrong/MultiReason-ChatAlpaca**: Optimizes complex reasoning performance in multi-turn dialogue scenarios. +- **Natural-Reasoning**: Enhances logical deduction for commonsense queries. +- **Reasoning-Instruction**: Structured reasoning instruction pairs. + +## 5. References +- **Developed by**: Jackrong +- **Base Model**: Llama-3.1-8B-Instruct +- **Training Framework**: Unsloth / TRL / PyTorch \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..a0cd3eb --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,17 @@ +{% if 'role' in messages[0] %}{% set msgs = messages | default([]) -%}{% set sys = (msgs | selectattr('role', 'equalto', 'system') | map(attribute='content') | list) -%}{{ bos_token }}<|start_header_id|>system<|end_header_id|> + +{{ (sys|length > 0) and (sys|join(' + +')) or ('You are a AI assistant. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: {Thought section} {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines') }}<|eot_id|>{% for m in msgs if m['role'] != 'system' %}<|start_header_id|>{{ m['role'] }}<|end_header_id|> + +{{ m['content'] }}<|eot_id|>{% endfor %}{% if add_generation_prompt %}<|start_header_id|>assistant<|end_header_id|> + +{% endif %}{% else %}{% set msgs = messages | default([]) -%}{% set sys = (msgs | selectattr('from', 'equalto', 'system') | map(attribute='value') | list) -%}{{ bos_token }}<|start_header_id|>system<|end_header_id|> + +{{ (sys|length > 0) and (sys|join(' + +')) or ('You are a AI assistant. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: {Thought section} {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines') }}<|eot_id|>{% for m in msgs if m['from'] != 'system' %}<|start_header_id|>{{ m['from'] }}<|end_header_id|> + +{{ m['value'] }}<|eot_id|>{% endfor %}{% if add_generation_prompt %}<|start_header_id|>assistant<|end_header_id|> + +{% endif %}{% endif %} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..7e722a3 --- /dev/null +++ b/config.json @@ -0,0 +1,38 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "torch_dtype": "bfloat16", + "eos_token_id": 128009, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 128004, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "transformers_version": "4.56.2", + "unsloth_fixed": true, + "unsloth_version": "2025.12.8", + "use_cache": true, + "vocab_size": 128256 +} \ No newline at end of file diff --git a/model-00001-of-00004.safetensors b/model-00001-of-00004.safetensors new file mode 100644 index 0000000..b4a2d1c --- /dev/null +++ b/model-00001-of-00004.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:641e52ca7c0f2b4903dee8b42a18f2c97444225c8db737e1af57c9661cd96ab7 +size 4976698672 diff --git a/model-00002-of-00004.safetensors b/model-00002-of-00004.safetensors new file mode 100644 index 0000000..b686610 --- /dev/null +++ b/model-00002-of-00004.safetensors @@ -0,0 +1,3 @@ +version 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The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines') }}<|eot_id|>{% for m in msgs if m['role'] != 'system' %}<|start_header_id|>{{ m['role'] }}<|end_header_id|>\n\n{{ m['content'] }}<|eot_id|>{% endfor %}{% if add_generation_prompt %}<|start_header_id|>assistant<|end_header_id|>\n\n{% endif %}{% else %}{% set msgs = messages | default([]) -%}{% set sys = (msgs | selectattr('from', 'equalto', 'system') | map(attribute='value') | list) -%}{{ bos_token }}<|start_header_id|>system<|end_header_id|>\n\n{{ (sys|length > 0) and (sys|join('\n\n')) or ('You are a AI assistant. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: {Thought section} {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. 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