Text Generation
Transformers
PyTorch
Chinese
English
llama
text2text-generation
text-generation-inference
Instructions to use ClueAI/ChatYuan-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClueAI/ChatYuan-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ClueAI/ChatYuan-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ClueAI/ChatYuan-7B") model = AutoModelForCausalLM.from_pretrained("ClueAI/ChatYuan-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ClueAI/ChatYuan-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ClueAI/ChatYuan-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ClueAI/ChatYuan-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ClueAI/ChatYuan-7B
- SGLang
How to use ClueAI/ChatYuan-7B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ClueAI/ChatYuan-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ClueAI/ChatYuan-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ClueAI/ChatYuan-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ClueAI/ChatYuan-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ClueAI/ChatYuan-7B with Docker Model Runner:
docker model run hf.co/ClueAI/ChatYuan-7B
Update README.md
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README.md
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language:
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ChatYuan-7B是一个支持中英双语的功能型对话语言大模型。它是基于LLama-7B模型上继续进行三阶段训练的模型。
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三阶段如下:
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1. 在中文通用语料上继续预训练500亿中文token
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为了遵守LLaMA模型许可证,我们将ChatYuan-7B权重发布为增量权重。您可以将我们的增量权重与原始的LLaMA权重相加,得到ChatYuan-7B权重。
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1. 通过原始[LLaMA-7B](https://github.com/facebookresearch/llama)生成LLaMA的hf模型(LLaMA-7B-HF),可以参考[指导](https://huggingface.co/docs/transformers/main/model_doc/llama), 也可以直接使用[llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf)
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2. 合并LLaMA-7B的hf模型和ChatYuan-7B模型
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### 合并脚本
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```shell
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python3 apply_delta.py --base ~/model_weights/LLaMA-7B-HF --delta ~/model_weights/ChatYuan-7B --target ~/model_weights/ChatYuan-7B-merge
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license: gpl-3.0
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tags:
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- text2text-generation
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pipeline_tag: text2text-generation
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language:
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- zh
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- en
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---
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ChatYuan-7B是一个支持中英双语的功能型对话语言大模型。它是基于LLama-7B模型上继续进行三阶段训练的模型。
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三阶段如下:
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1. 在中文通用语料上继续预训练500亿中文token
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为了遵守LLaMA模型许可证,我们将ChatYuan-7B权重发布为增量权重。您可以将我们的增量权重与原始的LLaMA权重相加,得到ChatYuan-7B权重。
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1. 通过原始[LLaMA-7B](https://github.com/facebookresearch/llama)生成LLaMA的hf模型(LLaMA-7B-HF),可以参考[指导](https://huggingface.co/docs/transformers/main/model_doc/llama), 也可以直接使用[llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf)
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2. 合并LLaMA-7B的hf模型和ChatYuan-7B模型成ChatYuan-7B-merge
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### 合并脚本
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```shell
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python3 apply_delta.py --base ~/model_weights/LLaMA-7B-HF --delta ~/model_weights/ChatYuan-7B --target ~/model_weights/ChatYuan-7B-merge
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