Add pipeline tag, library name, and improve model card (#1)

- Add pipeline tag, library name, and improve model card (9692008b31174c1853161dd0f94a7d4da222066c)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,199 +1,151 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
+license: cc-by-nc-4.0
+tags:
+- qwen
+- instruction-following
+- chinese
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Qwen2.5-Instruct-7B-COIG-P
 
+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).
 
 ## Model Details
 
-### Model Description
-
-This repository contains the Qwen2.5-Instruct-7B-COIG-P model of the paper [COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://huggingface.co/papers/2504.05535).
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
 - **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Funded by:** [More Information Needed]
+- **Shared by:** [More Information Needed]
+- **Model type:** Large Language Model (LLM)
+- **Language(s) (NLP):** Chinese (zh)
+- **License:** cc-by-nc-4.0
+- **Finetuned from model:** Qwen2
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
 - **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Paper:** [COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values](https://huggingface.co/papers/2504.05535)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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.
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+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.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+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.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Use the following code to get started with the Qwen2.5-Instruct-7B-COIG-P model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+device = "cuda"  # or "cpu" if you don't have a GPU
+
+model = AutoModelForCausalLM.from_pretrained(
+    "m-a-p/Qwen2.5-Instruct-7B-COIG-P",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("m-a-p/Qwen2.5-Instruct-7B-COIG-P")
+
+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.
+messages = [
+    {"role": "system", "content": "你是一个乐于助人的助手。"}, # You are a helpful assistant.
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+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.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+The model was trained using the [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory).
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+- **Training regime:** [More Information Needed]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+- **Checkpoint size:** [More Information Needed]
+- **Training time:** [More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
+The model's performance is evaluated using the Chinese Reward Benchmark (CRBench) and AlignBench.
 
-<!-- This should link to a Dataset Card if possible. -->
+#### Testing Data
 
-[More Information Needed]
+- Chinese Reward Benchmark (CRBench): [https://huggingface.co/datasets/m-a-p/COIG-P-CRM](https://huggingface.co/datasets/m-a-p/COIG-P-CRM)
+- AlignBench: [https://github.com/THUDM/AlignBench](https://github.com/THUDM/AlignBench)
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+[Add factors from paper, e.g., domain, task type]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+[Add metrics from paper, e.g., accuracy, precision, recall]
 
 ### Results
 
-[More Information Needed]
+[Add results from paper, including tables and figures if appropriate]
 
 #### Summary
 
+[Summarize evaluation results from the paper]
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@misc{pteam2025coigphighqualitylargescalechinese,
+      title={COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values}, 
+      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},
+      year={2025},
+      eprint={2504.05535},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.05535}, 
+}
+```
+
+**APA:** [Add APA citation here based on BibTeX]