Text Generation
Transformers
PyTorch
internlm
feature-extraction
conversational
custom_code
4-bit precision
awq
Instructions to use internlm/internlm2-chat-20b-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm2-chat-20b-4bits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="internlm/internlm2-chat-20b-4bits", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm2-chat-20b-4bits", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use internlm/internlm2-chat-20b-4bits with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/internlm2-chat-20b-4bits" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-20b-4bits", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/internlm/internlm2-chat-20b-4bits
- SGLang
How to use internlm/internlm2-chat-20b-4bits 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/internlm2-chat-20b-4bits" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-20b-4bits", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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/internlm2-chat-20b-4bits" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-20b-4bits", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use internlm/internlm2-chat-20b-4bits with Docker Model Runner:
docker model run hf.co/internlm/internlm2-chat-20b-4bits
Commit History
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Update config.json (#4)
Verified signature- Update config.json (1c5eeaa24ceb7d2d20ad71c0134ca198cb0e6cc7) Co-authored-by: BridgeEight <BridgeEight@users.noreply.huggingface.co>5d9bb20
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Update special tokens (#1)
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fix readme
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fix readme
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fix readme
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WIP: fix readme
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WIP: fix readme
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WIP: add readme of internlm2-chat-20b-4bits model
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initial commit of internlm2-chat-20b-4bits model
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update .gitattributes
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initial commit
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