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while-ai/community-identity-spec-aas-1.7b
community-identity-spec-aas-1.7b is a text generation model from while-ai. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
Recipe: recipes/community/identity-spec-no-unasked-maker-aas · Collection: Course and community runs
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From the Hugging Face model README
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.
| 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.
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 |
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
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.