Instructions to use Ailiance-fr/devstral-sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ailiance-fr/devstral-sql-lora with PEFT:
Task type is invalid.
- MLX
How to use Ailiance-fr/devstral-sql-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-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-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-lora" --prompt "Once upon a time"
docs: add base vs tuned bench comparison
Browse files
README.md
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## Related
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See the full [Ailiance-fr LoRA collection](https://huggingface.co/Ailiance-fr).
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## Related
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See the full [Ailiance-fr LoRA collection](https://huggingface.co/Ailiance-fr).
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## Bench comparison (2026-05-11)
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### Base model (Devstral-Small-2-24B-MLX-4bit) capability
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| Task | Score | Notes |
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| GSM8K-CoT flex EM | **0.96** | W3 lm-eval-harness (--limit 100) |
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| ARC-Easy acc / acc_norm | **0.80 / 0.75** | |
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| MMLU-Pro Computer Science | **0.64** | |
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Source: <https://github.com/ailiance/ailiance/tree/main/output/lm-eval-base-2026-05-11>
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### This LoRA (tuned) — bench PENDING
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Will include kicad-sch / iact-bench validators + W3 lm-eval delta. See spec for
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methodology:
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<https://github.com/ailiance/ailiance-bench/blob/main/docs/superpowers/specs/2026-05-11-kicad-sch-gap-design.md>
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