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vincentmin
/
bloomz-1b1-eli5-pretrained

Text Generation
Transformers
PyTorch
TensorBoard
bloom
Generated from Trainer
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use vincentmin/bloomz-1b1-eli5-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vincentmin/bloomz-1b1-eli5-pretrained with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="vincentmin/bloomz-1b1-eli5-pretrained")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("vincentmin/bloomz-1b1-eli5-pretrained")
    model = AutoModelForCausalLM.from_pretrained("vincentmin/bloomz-1b1-eli5-pretrained", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use vincentmin/bloomz-1b1-eli5-pretrained with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "vincentmin/bloomz-1b1-eli5-pretrained"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "vincentmin/bloomz-1b1-eli5-pretrained",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/vincentmin/bloomz-1b1-eli5-pretrained
  • SGLang

    How to use vincentmin/bloomz-1b1-eli5-pretrained 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 "vincentmin/bloomz-1b1-eli5-pretrained" \
        --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": "vincentmin/bloomz-1b1-eli5-pretrained",
    		"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 "vincentmin/bloomz-1b1-eli5-pretrained" \
            --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": "vincentmin/bloomz-1b1-eli5-pretrained",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use vincentmin/bloomz-1b1-eli5-pretrained with Docker Model Runner:

    docker model run hf.co/vincentmin/bloomz-1b1-eli5-pretrained
bloomz-1b1-eli5-pretrained
3.65 GB
Ctrl+K
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  • 1 contributor
History: 13 commits
vincentmin's picture
vincentmin
add checkpoint and move adapters to root
9a35581 over 3 years ago
  • check-2000
    add checkpoint and move adapters to root over 3 years ago
  • tensorboard
    add tensorboard logs over 3 years ago
  • .gitattributes
    1.53 kB
    Upload tokenizer over 3 years ago
  • .gitignore
    13 Bytes
    End of training over 3 years ago
  • README.md
    1.39 kB
    update model card README.md over 3 years ago
  • adapter_config.json
    388 Bytes
    add checkpoint and move adapters to root over 3 years ago
  • adapter_model.bin
    9.45 MB
    xet
    add checkpoint and move adapters to root over 3 years ago
  • config.json
    807 Bytes
    use model resulting from running merge_peft_adapter.py over 3 years ago
  • generation_config.json
    132 Bytes
    use model resulting from running merge_peft_adapter.py over 3 years ago
  • pytorch_model.bin
    2.14 GB
    xet
    use model resulting from running merge_peft_adapter.py over 3 years ago
  • special_tokens_map.json
    96 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    14.5 MB
    xet
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    286 Bytes
    Upload tokenizer over 3 years ago
  • training_args.bin
    3.58 kB
    xet
    End of training over 3 years ago