finetuning-technique

1# Finetuning Technique
2 
3Guides the user through selecting a fine-tuning technique based on their use case and validates compatibility with the selected model.
4 
5## When to Use
6 
7- User has decided to finetune and needs to choose a technique
8- User wants to change their finetuning technique
9- Technique needs to be validated against a selected model
10 
11## Prerequisites
12 
13- A base model has been selected (via model-selection skill). The model name and hub must be known.
14- A `use_case_spec.md` file exists. If not, activate the use-case-specification skill to generate it first.
15 
16## Workflow
17 
18### Step 1: Determine Finetuning Technique
19 
20Consult `references/finetune_technique_selection_guide.md` to recommend the best-fit technique based on the use case and the user's needs (SFT, DPO, RLVR, RLAIF).
21 
22Present the recommendation and reasoning to the user. Ask if they'd like to go with the recommendation or prefer a different technique.
23 
24### Step 2: Validate Technique Availability
25 
261. Once the user confirms a technique, retrieve the finetuning techniques available for the selected model by running: `python finetuning-technique/scripts/get_recipes.py <model-name> <hub-name>`
27   - This returns only the techniques the model actually supports, filtered to SFT, DPO, RLVR, and RLAIF. Only these four techniques are supported — ignore any other techniques even if the model's recipes include them.
282. If the chosen technique is available for the model, proceed to Step 3.
29…