I fine-tuned Devstral-Small-2-24B on 2,322 Claude 4.6 Opus <think>...</think>
reasoning traces to give it explicit chain-of-thought before writing code.
**Model:** https://huggingface.co/adamjen/Devstral-Small-2-24B-Opus-Reasoning
**Files available:**
- Q4_K_M GGUF (14.3GB)
- Q5_K_M GGUF (16.8GB) ← recommended
- LoRA adapter (370MB) for merging yourself
**Hardware used:** RTX 3090 24GB
**Framework:** Unsloth + QLoRA (r=16)
**Checkpoint:** End of epoch 2 (~1200 steps) — better generalisation than full epoch 3
The main challenge was that Devstral is a VLM (Pixtral vision encoder) which
made direct text-only training on 24GB impossible. Had to extract the Ministral3
language layers into a standalone text-only model first. Full write-up coming on
my blog.
Happy to answer questions about the training process.
Training data: nohurry/Opus-4.6-Reasoning-3000x-filtered — 2,322 samples of Claude 4.6 Opus reasoning traces,
filtered to <20k chars.
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