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hllj
/
BloomZ-7B1-Vi-Math

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

Instructions to use hllj/BloomZ-7B1-Vi-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use hllj/BloomZ-7B1-Vi-Math with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="hllj/BloomZ-7B1-Vi-Math")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("hllj/BloomZ-7B1-Vi-Math")
    model = AutoModelForCausalLM.from_pretrained("hllj/BloomZ-7B1-Vi-Math")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use hllj/BloomZ-7B1-Vi-Math with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "hllj/BloomZ-7B1-Vi-Math"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "hllj/BloomZ-7B1-Vi-Math",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/hllj/BloomZ-7B1-Vi-Math
  • SGLang

    How to use hllj/BloomZ-7B1-Vi-Math 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 "hllj/BloomZ-7B1-Vi-Math" \
        --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": "hllj/BloomZ-7B1-Vi-Math",
    		"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 "hllj/BloomZ-7B1-Vi-Math" \
            --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": "hllj/BloomZ-7B1-Vi-Math",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use hllj/BloomZ-7B1-Vi-Math with Docker Model Runner:

    docker model run hf.co/hllj/BloomZ-7B1-Vi-Math
BloomZ-7B1-Vi-Math
14.2 GB
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  • 1 contributor
History: 3 commits
hllj's picture
hllj
Upload BloomForCausalLM
6f91f35 over 2 years ago
  • .gitattributes
    1.57 kB
    Upload tokenizer over 2 years ago
  • config.json
    806 Bytes
    Upload BloomForCausalLM over 2 years ago
  • generation_config.json
    132 Bytes
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  • model-00001-of-00003.safetensors
    4.98 GB
    xet
    Upload BloomForCausalLM over 2 years ago
  • model-00002-of-00003.safetensors
    4.87 GB
    xet
    Upload BloomForCausalLM over 2 years ago
  • model-00003-of-00003.safetensors
    4.3 GB
    xet
    Upload BloomForCausalLM over 2 years ago
  • model.safetensors.index.json
    31.8 kB
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  • special_tokens_map.json
    552 Bytes
    Upload tokenizer over 2 years ago
  • tokenizer.json
    14.5 MB
    xet
    Upload tokenizer over 2 years ago
  • tokenizer_config.json
    983 Bytes
    Upload tokenizer over 2 years ago