Instructions to use while-ai/paper-harness-and-weights-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use while-ai/paper-harness-and-weights-1.5b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "while-ai/paper-harness-and-weights-1.5b") - Notebooks
- Google Colab
- Kaggle
paper-harness-and-weights-1.5b
Recipe: recipes/papers/harness-and-weights · Collection: Papers, replicated
Two levers on one task set: the harness (instructions, skills file, a Python tool) and the weights (GRPO with LoRA). Four cells, paired by task: neither, harness only, weights only, both. The harness was picked on the holdout before any training.
Result
2026-09-21, one H100, 79 held-out tasks in six families the training never showed, 4 rollouts a task, 40 GRPO steps per trained arm.
| Cell | harness | model | pass@1 | 95% CI |
|---|---|---|---|---|
| neither | 00_baseline |
base | 0.00 | [0.00, 0.01] |
| harness | 01_skills |
base | 0.09 | [0.05, 0.13] |
| weights | 00_baseline |
trained | 0.03 | [0.01, 0.04] |
| both | 01_skills |
trained | 0.09 | [0.06, 0.12] |
Picking the harness on the holdout moved the base +0.08 [+0.05, +0.12]. Training the weights under the bare harness moved it +0.03. Training under the chosen harness landed where the harness alone did on pass@1 and raised pass@4 from 0.09 to 0.30. One training seed per arm, so the trained pairs are unresolved. The recipe README has the full attribution table and both hack scans.
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 |
|---|---|
. |
both: trained under the 01_skills harness, 2026-09-21 run |
weights |
weights: trained under the bare 00_baseline harness, 2026-09-21 run |
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "while-ai/paper-harness-and-weights-1.5b") # the headline arm
model = PeftModel.from_pretrained(base, "while-ai/paper-harness-and-weights-1.5b", subfolder="weights") # another arm
Reproduce
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/papers/harness-and-weights
python recipe.py
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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Base model
Qwen/Qwen2.5-1.5B