Instructions to use while-ai/course-refunds-sft-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use while-ai/course-refunds-sft-1.5b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "while-ai/course-refunds-sft-1.5b") - Notebooks
- Google Colab
- Kaggle
course-refunds-sft-1.5b
Recipe: recipes/04-train/sft · Collection: Course and community runs
Lesson 7 of the course: SFT on the course's own 46-row export, on one A10G, in six minutes. Base pass@1 0.25 to 0.73 on 40 held-out tasks the training never saw.
Result
| pass@1 | 95% CI | pass^4 | pass@4 | |
|---|---|---|---|---|
| before (base, seed 1) | 0.25 | [0.16, 0.35] | 0.07 | 0.48 |
| after (this adapter) | 0.73 | [0.63, 0.81] | 0.41 | 0.95 |
Paired delta +0.481 [+0.342, +0.616] over 40 tasks. Three base passes gave run_std 0.002, so a delta under 0.011 is noise. Loss 3.07 to 0.80 over 40 steps, 35 seconds of training. The full log is in the recipe README under What you get.
Arms in this repo
The root holds the arm the recipe README's headline number reports. Every other arm is a subfolder named after it. checkpoints/ never ships.
| folder | arm |
|---|---|
. |
SFT, 40 steps, run lesson7-sft |
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "while-ai/course-refunds-sft-1.5b") # the headline arm
Reproduce
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/04-train/sft
modal run train_modal.py --data train.jsonl
The recipe README pins the seed, the library versions and the GPU, and its Checks table says what the eval verified. Read the Learned section before quoting a number from this card.
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