Instructions to use Ailiance-fr/devstral-sql-bf16-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ailiance-fr/devstral-sql-bf16-lora with PEFT:
Task type is invalid.
- MLX
How to use Ailiance-fr/devstral-sql-bf16-lora with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Ailiance-fr/devstral-sql-bf16-lora") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use Ailiance-fr/devstral-sql-bf16-lora with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Ailiance-fr/devstral-sql-bf16-lora" --prompt "Once upon a time"
- Xet hash:
- 3bf8d367d3ebaae956fdfa252577bf7b09d614e93e60377449e86668663595f8
- Size of remote file:
- 370 MB
- SHA256:
- 0ed0785f169808a80a8115e13290a44c809c10d5e328388575df463ce515ea9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.