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SpectraSuite
/
QuantLM_1.1B_4bit_Unpacked

Text Generation
Transformers
Safetensors
llama
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use SpectraSuite/QuantLM_1.1B_4bit_Unpacked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use SpectraSuite/QuantLM_1.1B_4bit_Unpacked with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SpectraSuite/QuantLM_1.1B_4bit_Unpacked")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("SpectraSuite/QuantLM_1.1B_4bit_Unpacked")
    model = AutoModelForCausalLM.from_pretrained("SpectraSuite/QuantLM_1.1B_4bit_Unpacked")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use SpectraSuite/QuantLM_1.1B_4bit_Unpacked with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "SpectraSuite/QuantLM_1.1B_4bit_Unpacked"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SpectraSuite/QuantLM_1.1B_4bit_Unpacked",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/SpectraSuite/QuantLM_1.1B_4bit_Unpacked
  • SGLang

    How to use SpectraSuite/QuantLM_1.1B_4bit_Unpacked 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 "SpectraSuite/QuantLM_1.1B_4bit_Unpacked" \
        --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": "SpectraSuite/QuantLM_1.1B_4bit_Unpacked",
    		"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 "SpectraSuite/QuantLM_1.1B_4bit_Unpacked" \
            --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": "SpectraSuite/QuantLM_1.1B_4bit_Unpacked",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use SpectraSuite/QuantLM_1.1B_4bit_Unpacked with Docker Model Runner:

    docker model run hf.co/SpectraSuite/QuantLM_1.1B_4bit_Unpacked
QuantLM_1.1B_4bit_Unpacked
2.3 GB
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  • 1 contributor
History: 4 commits
tejasexpress's picture
tejasexpress
Update config.json
8f3e7ec verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    854 Bytes
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  • config.json
    790 Bytes
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  • generation_config.json
    111 Bytes
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  • model.safetensors
    2.3 GB
    xet
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  • special_tokens_map.json
    441 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    4.83 kB
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