Instructions to use ECj/Yi-6B-200K-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ECj/Yi-6B-200K-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ECj/Yi-6B-200K-GPTQ")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ECj/Yi-6B-200K-GPTQ") model = AutoModelForCausalLM.from_pretrained("ECj/Yi-6B-200K-GPTQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ECj/Yi-6B-200K-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ECj/Yi-6B-200K-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ECj/Yi-6B-200K-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ECj/Yi-6B-200K-GPTQ
- SGLang
How to use ECj/Yi-6B-200K-GPTQ 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 "ECj/Yi-6B-200K-GPTQ" \ --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": "ECj/Yi-6B-200K-GPTQ", "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 "ECj/Yi-6B-200K-GPTQ" \ --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": "ECj/Yi-6B-200K-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ECj/Yi-6B-200K-GPTQ with Docker Model Runner:
docker model run hf.co/ECj/Yi-6B-200K-GPTQ
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README.md
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这里提供了共四个量化后的模型权重(下面按量化后的性能排序):
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- [AutoGPTQ-8bit-
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- [AutoAWQ-4bit-32gs](https://huggingface.co/ECj/Yi-6B-200K-AWQ/tree/main) 使用AWQ方式进行4bit量化,比GPTQ-4bit生成质量优秀,但兼容性不如GPTQ
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- [AutoGPTQ-4bit-32gs](https://huggingface.co/ECj/Yi-6B-200K-GPTQ/tree/main) 使用GPTQ方式进行4bit量化,使用了Group size 32,比默认设置量化的效果更优秀。
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- [AutoGPTQ-4bit-128gs](https://huggingface.co/ECj/Yi-6B-200K-GPTQ/tree/4bits-gps128-descactF) 使用GPTQ方式进行4bit量化,使用了AutoGPTQ的默认设置。
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这里提供了共四个量化后的模型权重(下面按量化后的性能排序):
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- [AutoGPTQ-8bit-32gs](https://huggingface.co/ECj/Yi-6B-200K-GPTQ/tree/8bits-gps32) 使用GPTQ方式进行8bit量化,拥有最高的生成质量。
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- [AutoAWQ-4bit-32gs](https://huggingface.co/ECj/Yi-6B-200K-AWQ/tree/main) 使用AWQ方式进行4bit量化,比GPTQ-4bit生成质量优秀,但兼容性不如GPTQ
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- [AutoGPTQ-4bit-32gs](https://huggingface.co/ECj/Yi-6B-200K-GPTQ/tree/main) 使用GPTQ方式进行4bit量化,使用了Group size 32,比默认设置量化的效果更优秀。
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- [AutoGPTQ-4bit-128gs](https://huggingface.co/ECj/Yi-6B-200K-GPTQ/tree/4bits-gps128-descactF) 使用GPTQ方式进行4bit量化,使用了AutoGPTQ的默认设置。
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