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Abhisek987
/
llama-3.2-sql-lora

Text Generation
Transformers
Safetensors
PEFT
English
llama
llama-3
sql
text-to-sql
lora
finetuned
Model card Files Files and versions
xet
Community

Instructions to use Abhisek987/llama-3.2-sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Abhisek987/llama-3.2-sql-lora with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Abhisek987/llama-3.2-sql-lora")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Abhisek987/llama-3.2-sql-lora", dtype="auto")
  • PEFT

    How to use Abhisek987/llama-3.2-sql-lora with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Abhisek987/llama-3.2-sql-lora with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Abhisek987/llama-3.2-sql-lora"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Abhisek987/llama-3.2-sql-lora",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Abhisek987/llama-3.2-sql-lora
  • SGLang

    How to use Abhisek987/llama-3.2-sql-lora 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 "Abhisek987/llama-3.2-sql-lora" \
        --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": "Abhisek987/llama-3.2-sql-lora",
    		"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 "Abhisek987/llama-3.2-sql-lora" \
            --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": "Abhisek987/llama-3.2-sql-lora",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Abhisek987/llama-3.2-sql-lora with Docker Model Runner:

    docker model run hf.co/Abhisek987/llama-3.2-sql-lora
llama-3.2-sql-lora
175 MB
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  • 1 contributor
History: 3 commits
Abhisek987's picture
Abhisek987
Update README.md
bbaebc6 verified 7 months ago
  • checkpoint-1311
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  • checkpoint-500
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  • .gitattributes
    1.52 kB
    initial commit 7 months ago
  • README.md
    4.99 kB
    Update README.md 7 months ago
  • adapter_config.json
    677 Bytes
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  • adapter_model.safetensors
    36.7 MB
    xet
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  • special_tokens_map.json
    335 Bytes
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  • tokenizer.json
    9.09 MB
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  • tokenizer_config.json
    50.5 kB
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  • training_args.bin

    Detected Pickle imports (9)

    • "transformers.training_args.TrainingArguments",
    • "transformers.trainer_utils.IntervalStrategy",
    • "transformers.trainer_pt_utils.AcceleratorConfig",
    • "transformers.training_args.OptimizerNames",
    • "transformers.trainer_utils.SchedulerType",
    • "accelerate.utils.dataclasses.DistributedType",
    • "transformers.trainer_utils.HubStrategy",
    • "accelerate.state.PartialState",
    • "torch.device"

    How to fix it?

    4.73 kB
    xet
    Upload folder using huggingface_hub 7 months ago