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Ammad1Ali
/
sql-code-gguf

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

Instructions to use Ammad1Ali/sql-code-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Ammad1Ali/sql-code-gguf with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Ammad1Ali/sql-code-gguf")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Ammad1Ali/sql-code-gguf")
    model = AutoModelForCausalLM.from_pretrained("Ammad1Ali/sql-code-gguf")
  • llama-cpp-python

    How to use Ammad1Ali/sql-code-gguf with llama-cpp-python:

    # !pip install llama-cpp-python
    
    from llama_cpp import Llama
    
    llm = Llama.from_pretrained(
    	repo_id="Ammad1Ali/sql-code-gguf",
    	filename="sqlcoder-7b-q5_k_m.gguf",
    )
    
    output = llm(
    	"Once upon a time,",
    	max_tokens=512,
    	echo=True
    )
    print(output)
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • llama.cpp

    How to use Ammad1Ali/sql-code-gguf with llama.cpp:

    Install from brew
    brew install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    # Run inference directly in the terminal:
    llama-cli -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama-server -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    # Run inference directly in the terminal:
    llama-cli -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf Ammad1Ali/sql-code-gguf:Q5_K_M
    Use Docker
    docker model run hf.co/Ammad1Ali/sql-code-gguf:Q5_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use Ammad1Ali/sql-code-gguf with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Ammad1Ali/sql-code-gguf"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Ammad1Ali/sql-code-gguf",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Ammad1Ali/sql-code-gguf:Q5_K_M
  • SGLang

    How to use Ammad1Ali/sql-code-gguf 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 "Ammad1Ali/sql-code-gguf" \
        --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": "Ammad1Ali/sql-code-gguf",
    		"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 "Ammad1Ali/sql-code-gguf" \
            --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": "Ammad1Ali/sql-code-gguf",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Ollama

    How to use Ammad1Ali/sql-code-gguf with Ollama:

    ollama run hf.co/Ammad1Ali/sql-code-gguf:Q5_K_M
  • Unsloth Studio new

    How to use Ammad1Ali/sql-code-gguf with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for Ammad1Ali/sql-code-gguf to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for Ammad1Ali/sql-code-gguf to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Ammad1Ali/sql-code-gguf to start chatting
  • Docker Model Runner

    How to use Ammad1Ali/sql-code-gguf with Docker Model Runner:

    docker model run hf.co/Ammad1Ali/sql-code-gguf:Q5_K_M
  • Lemonade

    How to use Ammad1Ali/sql-code-gguf with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull Ammad1Ali/sql-code-gguf:Q5_K_M
    Run and chat with the model
    lemonade run user.sql-code-gguf-Q5_K_M
    List all available models
    lemonade list
sql-code-gguf
4.79 GB
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  • 1 contributor
History: 2 commits
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Ammad1Ali
Upload 13 files
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  • .gitattributes
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  • config.json
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  • generation_config.json
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  • gitattributes
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  • label_mask.npy
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  • labels.npy
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  • model.safetensors.index.json
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  • predictions.npy
    3.66 MB
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  • special_tokens_map.json
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  • sqlcoder-7b-q5_k_m.gguf
    4.78 GB
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  • tokenizer.json
    1.84 MB
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  • tokenizer.model
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  • tokenizer_config.json
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  • training_args.bin

    Detected Pickle imports (8)

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

    How to fix it?

    4.86 kB
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