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pbhaskar
/
full_ft

Text Generation
Transformers
Safetensors
gemma3_text
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use pbhaskar/full_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use pbhaskar/full_ft with Transformers:

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

    How to use pbhaskar/full_ft with vLLM:

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

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

    How to use pbhaskar/full_ft with Docker Model Runner:

    docker model run hf.co/pbhaskar/full_ft
full_ft
4.04 GB
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  • 1 contributor
History: 3 commits
pbhaskar's picture
pbhaskar
Upload tokenizer
3e9b4c3 verified about 1 year ago
  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • config.json
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  • generation_config.json
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  • model.safetensors
    4 GB
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  • special_tokens_map.json
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  • tokenizer.json
    33.4 MB
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  • tokenizer.model
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  • tokenizer_config.json
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