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ZahrizhalAli
/
llama-7b-code-generation

Text Generation
Transformers
English
code
Model card Files Files and versions
xet
Community

Instructions to use ZahrizhalAli/llama-7b-code-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ZahrizhalAli/llama-7b-code-generation with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="ZahrizhalAli/llama-7b-code-generation")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ZahrizhalAli/llama-7b-code-generation", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use ZahrizhalAli/llama-7b-code-generation with vLLM:

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

    How to use ZahrizhalAli/llama-7b-code-generation 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 "ZahrizhalAli/llama-7b-code-generation" \
        --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": "ZahrizhalAli/llama-7b-code-generation",
    		"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 "ZahrizhalAli/llama-7b-code-generation" \
            --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": "ZahrizhalAli/llama-7b-code-generation",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ZahrizhalAli/llama-7b-code-generation with Docker Model Runner:

    docker model run hf.co/ZahrizhalAli/llama-7b-code-generation
llama-7b-code-generation
69.1 MB
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  • 1 contributor
History: 112 commits
ZahrizhalAli's picture
ZahrizhalAli
Update config.json
9a3a04f almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    377 Bytes
    Update README.md almost 3 years ago
  • adapter_config.json
    447 Bytes
    Training in progress, step 10 almost 3 years ago
  • adapter_model.bin
    67.2 MB
    xet
    Training in progress, step 400 almost 3 years ago
  • config.json
    1.03 kB
    Update config.json almost 3 years ago
  • modeling_flash_llama.py
    45.3 kB
    Create modeling_flash_llama.py almost 3 years ago
  • special_tokens_map.json
    437 Bytes
    Training in progress, step 10 almost 3 years ago
  • tokenizer.json
    1.84 MB
    Training in progress, step 10 almost 3 years ago
  • tokenizer_config.json
    762 Bytes
    Training in progress, step 10 almost 3 years ago
  • training_args.bin
    4.16 kB
    xet
    Training in progress, step 10 almost 3 years ago