初始化项目，由ModelHub XC社区提供模型
Model: LahiruWije/Qwen2.5-0.5B-Instruct-GPRO-GSM8K Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- unsloth
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- trl
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- grpo
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- reasoning
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- gsm8k
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datasets:
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- openai/gsm8k
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-0.5B-Instruct
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pipeline_tag: question-answering
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license: apache-2.0
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---
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# Model Card for Qwen2.5-0.5B-Instruct-GSM8K-Reasoning
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<!-- Provide a quick summary of what the model is/does. -->
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This model is a fine-tuned version of the **Qwen2.5-0.5B-Instruct** model, specifically adapted for **mathematical reasoning tasks** using the **GSM8K dataset**. It leverages **GPRO (Generalized Policy Optimization for Reasoning)** methods, as described in the *DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models* paper, to enhance its reasoning capabilities. The fine-tuning process was performed using **Unsloth** for efficiency and **TRL (Transformer Reinforcement Learning)** for reinforcement learning-based training.
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## Model Details
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## How to Get Started with the Model
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Use the code below to load and use the model with vLLM & Unsloth:
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```python
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from unsloth import FastLanguageModel
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from vllm import SamplingParams
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import torch
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# Load the Model & Tokenizer
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "AdamLucek/Qwen2.5-3B-Instruct-GRPO-2K-GSM8K",
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max_seq_length = 2048,
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load_in_4bit = True,
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fast_inference = True,
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gpu_memory_utilization = 0.7,
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)
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# Prep the Message
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PROMPT = "How many r's are in the word strawberry?"
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SYSTEM_PROMPT = """
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A conversation between User and Assistant. The user asks a question,
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and the Assistant solves it. The assistant first thinks about the
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reasoning process in the mind and then provides the user with the answer.
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Respond in the following format:
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<reasoning>
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...
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</reasoning>
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<answer>
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...
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</answer>
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"""
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text = tokenizer.apply_chat_template([
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{"role" : "system", "content" : SYSTEM_PROMPT},
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{"role" : "user", "content" : PROMPT},
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], tokenize = False, add_generation_prompt = True)
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# Generate a response
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sampling_params = SamplingParams(
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temperature = 0.8,
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top_p = 0.95,
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max_tokens = 1024,
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)
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output = model.fast_generate(
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text,
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sampling_params = sampling_params,
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)[0].outputs[0].text
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```
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### Model Description
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- **Model type:** Transformer-based language model fine-tuned for mathematical reasoning.
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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## Uses
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### Direct Use
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This model is intended for **mathematical reasoning tasks**, particularly for solving grade-school-level math problems as found in the GSM8K dataset. It can be used directly for question-answering tasks involving arithmetic and reasoning.
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### Downstream Use [optional]
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The model can be fine-tuned further for specific applications, such as tutoring systems, automated problem-solving tools, or other educational technologies.
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### Out-of-Scope Use
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This model is not designed for:
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- High-level mathematical research or advanced problem-solving.
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- Non-mathematical reasoning tasks without additional fine-tuning.
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- Applications requiring high precision in domains outside its training data.
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## Bias, Risks, and Limitations
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- **Bias:** The model may inherit biases present in the GSM8K dataset or the base model.
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- **Risks:** Incorrect reasoning or answers in critical applications (e.g., education or finance) could lead to misinformation.
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- **Limitations:** The model's performance is constrained by the quality and scope of the GSM8K dataset and the base model's capabilities.
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### Recommendations
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Users should:
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- Validate the model's outputs for critical applications.
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- Fine-tune the model further for domain-specific tasks.
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- Be aware of potential biases and limitations in reasoning capabilities.
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## Citations
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Cite GRPO as:
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```bibtex
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@article{zhihong2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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config.json
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|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"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|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|vision_pad|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user