Instructions to use qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qdtomassi/llama-3-sqlcoder-8b-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- 9253a42d19d90e6fe97cae6178f602d4d6dfb8e8a2cfd062f27e76e8978da166
- Size of remote file:
- 4.52 GB
- SHA256:
- 2058ec8758ee98ef6bf39988b661fe25210f396f6b93e4f48665c2c5b54932eb
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