Instructions to use ucalyptus/llama-3-sqlcoder-8b-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ucalyptus/llama-3-sqlcoder-8b-MLX 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("ucalyptus/llama-3-sqlcoder-8b-MLX") 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 ucalyptus/llama-3-sqlcoder-8b-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ucalyptus/llama-3-sqlcoder-8b-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ucalyptus/llama-3-sqlcoder-8b-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucalyptus/llama-3-sqlcoder-8b-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
ucalyptus/llama-3-sqlcoder-8b-MLX
This model was converted to MLX format from defog/llama-3-sqlcoder-8b.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ucalyptus/llama-3-sqlcoder-8b-MLX")
response = generate(model, tokenizer, prompt="hello", verbose=True)
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