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lelapa
/
InkubaLM-0.4B

Text Generation
Transformers
PyTorch
llama
nlp
InkubaLM
africanLLM
africa
llm
custom_code
text-generation-inference
Model card Files Files and versions
xet
Community
8

Instructions to use lelapa/InkubaLM-0.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lelapa/InkubaLM-0.4B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="lelapa/InkubaLM-0.4B", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("lelapa/InkubaLM-0.4B", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained("lelapa/InkubaLM-0.4B", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use lelapa/InkubaLM-0.4B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "lelapa/InkubaLM-0.4B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "lelapa/InkubaLM-0.4B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/lelapa/InkubaLM-0.4B
  • SGLang

    How to use lelapa/InkubaLM-0.4B 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 "lelapa/InkubaLM-0.4B" \
        --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": "lelapa/InkubaLM-0.4B",
    		"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 "lelapa/InkubaLM-0.4B" \
            --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": "lelapa/InkubaLM-0.4B",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use lelapa/InkubaLM-0.4B with Docker Model Runner:

    docker model run hf.co/lelapa/InkubaLM-0.4B

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Preview of files found in this repository
  • .gitattributes
    1.57 kB
    Upload main_image.png 5 months ago
  • InkubaLM.png
    314 kB
    Upload InkubaLM.png almost 2 years ago
  • README.md
    6.36 kB
    Update README.md over 1 year ago
  • config.json
    763 Bytes
    The current checkpoint doesn't use group query attention. (#3) almost 2 years ago
  • config_vulavulaslm.py
    9.04 kB
    Rename configuration_vulavulaslm.py to config_vulavulaslm.py about 2 years ago
  • generation_config.json
    111 Bytes
    adding SLM about 2 years ago
  • main_image.png
    2.19 MB
    xet
    Upload main_image.png 5 months ago
  • pytorch_model.bin

    Detected Pickle imports (5)

    • "torch.Tensor",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch._tensor._rebuild_from_type_v2"

    How to fix it?

    2.66 GB
    xet
    adding SLM about 2 years ago
  • special_tokens_map.json
    414 Bytes
    adding SLM about 2 years ago
  • tokenizer.json
    3.95 MB
    adding SLM about 2 years ago
  • tokenizer.model
    991 kB
    xet
    adding SLM about 2 years ago
  • tokenizer_config.json
    960 Bytes
    adding SLM about 2 years ago
  • vulavulaslm.py
    42.2 kB
    Updated Flash Attention Custom Code almost 2 years ago