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internlm
/
internlm2_5-1_8b-chat

Text Generation
Transformers
Safetensors
internlm2
feature-extraction
conversational
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use internlm/internlm2_5-1_8b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use internlm/internlm2_5-1_8b-chat with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="internlm/internlm2_5-1_8b-chat", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("internlm/internlm2_5-1_8b-chat", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use internlm/internlm2_5-1_8b-chat with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "internlm/internlm2_5-1_8b-chat"
    # 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_5-1_8b-chat",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/internlm/internlm2_5-1_8b-chat
  • SGLang

    How to use internlm/internlm2_5-1_8b-chat 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_5-1_8b-chat" \
        --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_5-1_8b-chat",
    		"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_5-1_8b-chat" \
            --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_5-1_8b-chat",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use internlm/internlm2_5-1_8b-chat with Docker Model Runner:

    docker model run hf.co/internlm/internlm2_5-1_8b-chat
internlm2_5-1_8b-chat
3.78 GB
Ctrl+K
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  • 2 contributors
History: 10 commits
x54-729
replace get_max_length
57dcbc5 about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    17.2 kB
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  • config.json
    992 Bytes
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  • configuration_internlm2.py
    8.84 kB
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  • generation_config.json
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  • model-00001-of-00002.safetensors
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  • model-00002-of-00002.safetensors
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  • model.safetensors.index.json
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  • modeling_internlm2.py
    81 kB
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  • special_tokens_map.json
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  • tokenization_internlm2.py
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  • tokenization_internlm2_fast.py
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  • tokenizer.model
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  • tokenizer_config.json
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