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Model: m-a-p/Qwen2.5-Instruct-7B-COIG-P Source: Original Platform
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---
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library_name: transformers
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pipeline_tag: text-generation
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license: cc-by-nc-4.0
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tags:
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- qwen
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- instruction-following
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- chinese
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---
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# Model Card for Qwen2.5-Instruct-7B-COIG-P
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This repository contains the Qwen2.5-Instruct-7B-COIG-P model, a 7B parameter Large Language Model fine-tuned for instruction following using the COIG-P dataset, as described in the paper [COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://huggingface.co/papers/2504.05535).
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## Model Details
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- **Developed by:** [More Information Needed]
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- **Funded by:** [More Information Needed]
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- **Shared by:** [More Information Needed]
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- **Model type:** Large Language Model (LLM)
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- **Language(s) (NLP):** Chinese (zh)
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- **License:** cc-by-nc-4.0
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- **Finetuned from model:** Qwen2
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### Model Sources
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- **Repository:** [More Information Needed]
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- **Paper:** [COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://huggingface.co/papers/2504.05535)
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## Uses
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### Direct Use
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This model is designed for text generation tasks and is particularly well-suited for Chinese language processing. It can be used for generating creative text formats, translating languages, and answering questions.
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### Downstream Use
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The model can be fine-tuned for various downstream tasks, including chatbots, code generation, summarization, question answering, and other NLP tasks. The [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) can be used for fine-tuning the model.
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### Out-of-Scope Use
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The model's performance may be limited when applied to tasks significantly different from those it was trained on or tasks requiring understanding of languages other than Chinese.
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## Bias, Risks, and Limitations
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The model may exhibit biases present in its training data, particularly reflecting biases inherent in the Chinese language and culture. Users should be aware of potential biases and limitations and use the model responsibly and ethically, avoiding applications that could perpetuate or amplify harmful biases.
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## How to Get Started with the Model
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Use the following code to get started with the Qwen2.5-Instruct-7B-COIG-P model:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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device = "cuda" # or "cpu" if you don't have a GPU
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model = AutoModelForCausalLM.from_pretrained(
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"m-a-p/Qwen2.5-Instruct-7B-COIG-P",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("m-a-p/Qwen2.5-Instruct-7B-COIG-P")
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prompt = "给我一个大型语言模型的简短介绍。" # Give me a short introduction to large language model.
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messages = [
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{"role": "system", "content": "你是一个乐于助人的助手。"}, # You are a helpful assistant.
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## Training Details
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### Training Data
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The model was trained on the COIG-P dataset ([https://huggingface.co/datasets/m-a-p/COIG-P](https://huggingface.co/datasets/m-a-p/COIG-P)). This dataset consists of 101k Chinese preference pairs across six domains: Chat, Code, Math, Logic, Novel, and Role.
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### Training Procedure
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The model was trained using the [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory).
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed]
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#### Speeds, Sizes, Times
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- **Checkpoint size:** [More Information Needed]
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- **Training time:** [More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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The model's performance is evaluated using the Chinese Reward Benchmark (CRBench) and AlignBench.
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#### Testing Data
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- Chinese Reward Benchmark (CRBench): [https://huggingface.co/datasets/m-a-p/COIG-P-CRM](https://huggingface.co/datasets/m-a-p/COIG-P-CRM)
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- AlignBench: [https://github.com/THUDM/AlignBench](https://github.com/THUDM/AlignBench)
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#### Factors
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[Add factors from paper, e.g., domain, task type]
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#### Metrics
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[Add metrics from paper, e.g., accuracy, precision, recall]
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### Results
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[Add results from paper, including tables and figures if appropriate]
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#### Summary
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[Summarize evaluation results from the paper]
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## Citation
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**BibTeX:**
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```bibtex
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@misc{pteam2025coigphighqualitylargescalechinese,
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title={COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values},
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author={P Team and Siwei Wu and Jincheng Ren and Xinrun Du and Shuyue Guo and Xingwei Qu and Yiming Liang and Jie Liu and Yunwen Li and Tianyu Zheng and Boyu Feng and Huaqing Yuan and Zenith Wang and Jiaheng Liu and Wenhao Huang and Chenglin Cai and Haoran Que and Jian Yang and Yuelin Bai and Zekun Moore Wang and Zhouliang Yu and Qunshu Lin and Ding Pan and Yuchen Jiang and Tiannan Wang and Wangchunshu Zhou and Shenzhi Wang and Xingyuan Bu and Minghao Liu and Guoyin Wang and Ge Zhang and Chenghua Lin},
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year={2025},
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eprint={2504.05535},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2504.05535},
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}
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```
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**APA:** [Add APA citation here based on BibTeX]
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31
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Normal file
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209
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|
||||
"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": 6000,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"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