ellora-skill

Fine-tune a model for one capability with a LoRA adapter, using a recipe from codelion/ellora. Every recipe follows the same shape: the model (or a tool) generates the training data, a LoRA adapter is trained on it, and the adapter is compared with the base model on held-out data. Seven recipes, each with a reference file and a script in scripts/.

1. Pick the recipe

The user wants to...	Recipe	Method	Notebook base model
Get a 4-bit model's quality back toward full precision	1: quantization recovery	Self-distillation, FP16 teacher to NF4 student	Qwen/Qwen3-0.6B
Make a model reason step by step in <think> tags	2: reasoning GRPO	Self-scored preference pairs, pairwise loss (named GRPO in the repo)	google/gemma-3-1b-it
Teach tool or function calling over a codebase	3: tool calling	Scripted scenarios + real tool execution, SFT	meta-llama/Llama-3.2-1B-Instruct (gated)
Handle long inputs such as whole repositories	4: long context	Progressive context curriculum	Qwen2.5-Coder-0.5B-Instruct
Write secure code by default	5: secure code	Preference pairs scored by Semgrep and regex rules, pairwise loss	Qwen2.5-Coder-0.5B-Instruct
Predict how code executes (variable states per line)	6: execution world model	GRPO on real Python execution traces	Qwen3-4B-Thinking-2507
Fine-tune on a new domain with no dataset yet	7: invented dataset	Rows invented by DataInventor, SFT	Qwen2.5-1.5B-Instruct

Open the matching reference before doing anything else. It has the data method, exact settings, what the notebook measured, the published adapter, and the pitfalls. If no recipe fits, recipe 7 is the general pattern: describe or collect prompt/completion rows, train with loss on replies only, evaluate on rows held out from training.

If the user only wants to use an existing Ellora adapter rather than train one, the reference lists its Hub name; load it with PeftModel.from_pretrained(base, adapter).

2. Check the hardware first

State this plainly to the user before starting:

• The notebooks target an NVIDIA GPU (Colab T4 or L4; recipe 4 ran on an H100). Recipes 1 to 6 load models in 4-bit (bitsandbytes or Unsloth), and some also use vLLM or flash-attention. None of these run on Apple silicon or CPU as written.
• Recipe 7's PyTorch script runs on CUDA, and on CPU in fp32 (slow).
• On an Apple silicon Mac, use scripts/recipe_7_mlx.py (mlx-lm LoRA). It is the only MLX route in this skill and the only one smoke-tested on a Mac. For other recipes on a Mac, say that they need a CUDA machine (Colab works) rather than improvising a port.
• Check what is already running before starting a long job (nvidia-smi, or ps for python/mlx processes on a Mac). Training competes with any model server on the machine.

Each script prints its options with --help and needs no packages for that. The requirements are listed at the top of each script. Tell the user what is missing and install only with their OK, into a virtual environment, never globally.

3. The shared workflow

1. Data. Generate it the recipe's way, or reuse the dataset the notebook published (most scripts do this by default; regenerating can take hours). Look at 5 to 10 rows before training. Say where the data came from.
2. Baseline. Score the base model on the held-out set first, with the same settings the adapter will get. Without a baseline there is no result.
3. Train. Start small (fewer samples, one epoch) to check that loss falls and memory fits, then run the full settings.
4. Evaluate. Same held-out data, same decoding settings, base vs adapter. Read some generations side by side; metrics like perplexity or ROUGE-L measure closeness to reference text, not correctness.
5. Publish (optional). Pushing to the Hugging Face Hub happens only with --push-to-hub REPO and only after the user asks for it and names the repo.

4. Ask before

• Downloading model weights or datasets (name the model and its size).
• Sending data to a hosted service (recipe 7's DataInventor app, an LLM judge endpoint).
• Running model-written code. Recipes 3, 5 and 6 execute generated code or tools; run them in a container or throwaway environment.
• Pushing anything to the Hub.

5. Report

Give the base and adapter numbers side by side on the same held-out data, the model, sample counts, steps and wall time, and where the adapter was saved. Quote the notebook's own results only as what that notebook measured, never as an expected outcome. Name what was not evaluated.

Files

• references/recipe-N-*.md: one per recipe.
• scripts/recipe_N_*.py: script versions of the notebooks (CUDA unless stated).
• scripts/recipe_7_mlx.py: recipe 7 on Apple silicon with mlx-lm.