Instructions to use while-ai/community-identity-spec-aas-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use while-ai/community-identity-spec-aas-1.7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "while-ai/community-identity-spec-aas-1.7b") - Notebooks
- Google Colab
- Kaggle
community-identity-spec-aas-1.7b
Recipe: recipes/community/identity-spec-no-unasked-maker-aas · Collection: Course and community runs
Three data selectors on the same pool, one behaviour under test: name the maker when asked, never when not. random is the neutral reference, loss (highest response loss first) is the baseline, aas caps the identity share at the pool's. Selection is an SDK program; the GPU only confirms it.
Result, from the recipe README
| arm | identity ask, names the maker | bait ask, no unasked maker |
|---|---|---|
| base, 3 passes | 0.005 / 0.000 / 0.000 | 1.000 / 1.000 / 1.000 |
random |
0.000 | 1.000 |
loss (baseline) |
0.930, +0.925 [+0.885, +0.960] | 0.588, -0.412 [-0.549, -0.294] |
aas (method) |
0.180, +0.175 [+0.125, +0.230] | 0.980, -0.020 [-0.059, +0.000] |
| method - baseline | -0.750 [-0.805, -0.690] | +0.392 [+0.275, +0.529] |
The loss selector learned the identity and leaked it into replies nobody asked; the capped selector learned less of it and leaked almost none. eval.json and pool_meta.json are the evaluation and the pool the selectors drew from.
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 |
|---|---|
. |
aas: highest loss first, identity share capped |
loss |
loss: highest loss first, the baseline |
random |
random: uniform draw, the reference |
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base, "while-ai/community-identity-spec-aas-1.7b") # the headline arm
model = PeftModel.from_pretrained(base, "while-ai/community-identity-spec-aas-1.7b", subfolder="loss") # another arm
Reproduce
git clone https://github.com/whilehq/whileai-sdk && cd whileai-sdk/recipes/community/identity-spec-no-unasked-maker-aas
python run.py && modal run train_modal.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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