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i3-lab
/
i3-22m

Text Generation
Transformers
PyTorch
Safetensors
English
i3
conversational
efficient
i3-architecture
Model card Files Files and versions
xet
Community
1

Instructions to use i3-lab/i3-22m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use i3-lab/i3-22m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="i3-lab/i3-22m")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("i3-lab/i3-22m", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use i3-lab/i3-22m with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "i3-lab/i3-22m"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "i3-lab/i3-22m",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/i3-lab/i3-22m
  • SGLang

    How to use i3-lab/i3-22m 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 "i3-lab/i3-22m" \
        --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": "i3-lab/i3-22m",
    		"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 "i3-lab/i3-22m" \
            --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": "i3-lab/i3-22m",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use i3-lab/i3-22m with Docker Model Runner:

    docker model run hf.co/i3-lab/i3-22m
i3-22m
182 MB
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  • 2 contributors
History: 16 commits
FlameF0X's picture
FlameF0X
Update README.md
a91ff3c verified 7 months ago
  • .gitattributes
    1.52 kB
    initial commit 7 months ago
  • README.md
    5.22 kB
    Update README.md 7 months ago
  • config.json
    322 Bytes
    Upload 6 files 7 months ago
  • model.safetensors
    90.6 MB
    xet
    Adding `safetensors` variant of this model (#1) 7 months ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage"

    What is a pickle import?

    90.8 MB
    xet
    Upload 6 files 7 months ago
  • special_tokens_map.json
    2 Bytes
    Upload 6 files 7 months ago
  • tokenizer.json
    123 kB
    Upload 6 files 7 months ago
  • tokenizer_config.json
    191 Bytes
    Upload 6 files 7 months ago
  • user.py
    7.93 kB
    Create user.py 7 months ago