Text Generation
PEFT
Safetensors
lora
character-training
voice
style
synthetic-data
while-ai
conversational
Instructions to use while-ai/retail-concise-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use while-ai/retail-concise-4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "while-ai/retail-concise-4b") - Notebooks
- Google Colab
- Kaggle
Zero Proof AI commited on
Re-measured at a non-binding cap with finish_reason recorded
Browse files
README.md
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# zeroproof-retail-concise-4b
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**A Qwen3-4B retail agent that answers first and stops, with nothing in the
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prompt telling it to.**
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11.8% for the base, with no increase in omitted information.
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The companion to
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| In the register | 0.118 | 0.
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| Omitted required information | 0.015 | 0.015 |
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| Median reply length | 442 chars | 201 chars |
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Delta +0.
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p =
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Register judged by Phi-4, a different model family from the policy. Omission
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decided in code against the identifiers each question requires.
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### Answer-production rate
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The register rate is conditional on the model having produced an answer at
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# zeroproof-retail-concise-4b
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**A Qwen3-4B retail agent that answers first and stops, with nothing in the
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prompt telling it to.** 83.8% of held-out replies are in the register against
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11.8% for the base, with no increase in omitted information.
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The companion to
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| | Base | This adapter |
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| In the register | 0.118 | 0.838 |
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| Omitted required information | 0.015 | 0.015 |
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| Median reply length | 442 chars | 201 chars |
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Delta +0.721, paired bootstrap over prompts, 95% CI **[+0.647, +0.794]**.
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98 prompts improved, 0 regressed, 36 unchanged. Sign test one-sided
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p = 3.2e-30. At 136 prompts the eval resolves +0.074 or larger.
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Register judged by Phi-4, a different model family from the policy. Omission
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decided in code against the identifiers each question requires.
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**Truncation, measured not inferred.** `finish_reason` is recorded per row.
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On this evaluation every reply in both arms finished on its own: 0.0% not-EOS
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base, 0.0% not-EOS trained, an inter-arm gap of 0.0 points, with the token cap
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at 2048 where neither arm reaches it. Base replies run to a median of 437
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characters and a 95th percentile of 2083, so the cap is not shaping the
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quantity being judged.
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The cap is not a neutral parameter when the trait is concision: too low it
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clips the base toward brevity and understates the gain, too high it lets the
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base ramble and inflates it. Re-measuring at 2048 against an earlier 700 moved
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the airline delta not at all (+0.899 both) and retail by -0.014, so the cap was
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doing no work in either direction.
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### Answer-production rate
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The register rate is conditional on the model having produced an answer at
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