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while-ai
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Model Details
Model Provider
while-ai
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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.
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 |
python
from peft import PeftModelfrom 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 armbash
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/04-train/sftmodal run train_modal.py --data train.jsonlThe 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.