Instructions to use internlm/internlm-chat-20b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm-chat-20b-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="internlm/internlm-chat-20b-4bit", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm-chat-20b-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use internlm/internlm-chat-20b-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/internlm-chat-20b-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm-chat-20b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/internlm/internlm-chat-20b-4bit
- SGLang
How to use internlm/internlm-chat-20b-4bit 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 "internlm/internlm-chat-20b-4bit" \ --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": "internlm/internlm-chat-20b-4bit", "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 "internlm/internlm-chat-20b-4bit" \ --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": "internlm/internlm-chat-20b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use internlm/internlm-chat-20b-4bit with Docker Model Runner:
docker model run hf.co/internlm/internlm-chat-20b-4bit
Commit History
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Update config.json
92e6687 -
restore assert info
7d12c84 -
Support dynamic ntk rope
d004015 -
Support dynamic ntk rope
58e0cf9 -
Support dynamic ntk rope
aae683a
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Update modeling_internlm.py (#11)
- Update modeling_internlm.py (562c7324f272e236ca7817eee1281703ac607474) - update assert (cd68ed8c4ed032bc9fd6a8a9b672145b5e5d13f6) Co-authored-by: - <irexyc@users.noreply.huggingface.co>218d304
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fix pad_token_id
a70f170 -
Fix error and update benchmark in README
86edf4c
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support transformers 4.34.0 (#9)
- support transformers 4.34.0 (9677d6891c132f995f40471efe64b6958f3469f3)5bb97d2
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Update README.md
24815bc
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check-in 4-bit weights of internlm-chat-20b
ecfdae4 -
initial commit
ecaa2ed