Instructions to use MaziyarPanahi/sqlcoder-7b-2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaziyarPanahi/sqlcoder-7b-2-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MaziyarPanahi/sqlcoder-7b-2-GGUF", dtype="auto") - llama-cpp-python
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MaziyarPanahi/sqlcoder-7b-2-GGUF", filename="sqlcoder-7b-2.Q2_K.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 MaziyarPanahi/sqlcoder-7b-2-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_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 MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_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 MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/sqlcoder-7b-2-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaziyarPanahi/sqlcoder-7b-2-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
- SGLang
How to use MaziyarPanahi/sqlcoder-7b-2-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 "MaziyarPanahi/sqlcoder-7b-2-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": "MaziyarPanahi/sqlcoder-7b-2-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 "MaziyarPanahi/sqlcoder-7b-2-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": "MaziyarPanahi/sqlcoder-7b-2-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
- Unsloth Studio new
How to use MaziyarPanahi/sqlcoder-7b-2-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 MaziyarPanahi/sqlcoder-7b-2-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 MaziyarPanahi/sqlcoder-7b-2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MaziyarPanahi/sqlcoder-7b-2-GGUF to start chatting
- Docker Model Runner
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
- Lemonade
How to use MaziyarPanahi/sqlcoder-7b-2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/sqlcoder-7b-2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.sqlcoder-7b-2-GGUF-Q4_K_M
List all available models
lemonade list
beam search with llama_cpp_python?
Were you able to use beam search with llama cpp python bindings? As the original model card says, it works better with num_beams=4.
Were you able to use beam search with llama cpp python bindings? As the original model card says, it works better with num_beams=4.
That's an interesting question. I suppose it is possible to use n_beam to see how it works, but I have never tested it myself: https://github.com/abetlen/llama-cpp-python/blob/a7281994d87927e42d8e636295c786057e98d8fe/llama_cpp/llama_cpp.py#L2564
That's an interesting question. I suppose it is possible to use
n_beamto see how it works, but I have never tested it myself: https://github.com/abetlen/llama-cpp-python/blob/a7281994d87927e42d8e636295c786057e98d8fe/llama_cpp/llama_cpp.py#L2564
By default the llama cpp python doesn't provide any api for beam search, only the available llama.cpp apis are made available through ctypes bindings. I tried reproducing this https://github.com/ggerganov/llama.cpp/blob/master/examples/beam-search/beam-search.cpp in python but got stuck.
Interesting, did not know that! Is this something we can ask as a feature-request in the Llama.cpp for Python?
Definitely. I was thinking to implement myself. There are two ways to approach it.
- implement beam_search algorithm in python
- Use the cpp llama_beam_search api using ctypes
As of now I have tried the 2nd approach which gives GGML_ASSERT_ERROR n_tokens<=n_batch. By increasing n_batch, it works and gives some output but its garbage. I know I should be discussing this on llama_cpp_python/llama.cpp repos but still sharing the insights here for all.
This is very interesting! Thanks for sharing your progress here @tanmaymane18
Shall we also start talking to the team in llama_cpp_python? It seems like an interesting feature to have
Just found a thread on python repo https://github.com/abetlen/llama-cpp-python/pull/631
Author's response: https://github.com/abetlen/llama-cpp-python/pull/631#issuecomment-1887992757