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MinimaLabs
/
KLM-1.6

Text Generation
Transformers
Safetensors
English
klm
custom_code
small-language-model
base
pretrained
hybrid
gated-deltanet
Model card Files Files and versions
xet
Community

Instructions to use MinimaLabs/KLM-1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MinimaLabs/KLM-1.6 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="MinimaLabs/KLM-1.6", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("MinimaLabs/KLM-1.6", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use MinimaLabs/KLM-1.6 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "MinimaLabs/KLM-1.6"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "MinimaLabs/KLM-1.6",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/MinimaLabs/KLM-1.6
  • SGLang

    How to use MinimaLabs/KLM-1.6 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 "MinimaLabs/KLM-1.6" \
        --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": "MinimaLabs/KLM-1.6",
    		"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 "MinimaLabs/KLM-1.6" \
            --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": "MinimaLabs/KLM-1.6",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use MinimaLabs/KLM-1.6 with Docker Model Runner:

    docker model run hf.co/MinimaLabs/KLM-1.6
KLM-1.6
396 MB
Ctrl+K
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  • 1 contributor
History: 7 commits
Eclipse-Senpai's picture
Eclipse-Senpai
KLM 1.6 weights
5322676 verified about 6 hours ago
  • charts
    KLM 1.6 card and code about 7 hours ago
  • .gitattributes
    1.52 kB
    initial commit about 7 hours ago
  • LICENSE
    11.4 kB
    KLM 1.6 card and code about 7 hours ago
  • README.md
    6.73 kB
    Tighten leaderboard sentence about 6 hours ago
  • config.json
    773 Bytes
    KLM 1.6 card and code about 7 hours ago
  • configuration_klm.py
    2.02 kB
    KLM 1.6 card and code about 7 hours ago
  • generation_config.json
    798 Bytes
    KLM 1.6 card and code about 7 hours ago
  • model.safetensors
    396 MB
    xet
    KLM 1.6 weights about 6 hours ago
  • modeling_klm.py
    20.5 kB
    KLM 1.6 card and code about 7 hours ago
  • special_tokens_map.json
    181 Bytes
    KLM 1.6 card and code about 7 hours ago
  • tokenizer.json
    264 kB
    KLM 1.6 card and code about 7 hours ago
  • tokenizer_config.json
    418 Bytes
    Tokenizer returns only the model's inputs about 7 hours ago