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teacher57
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Model Details
Model Provider
teacher57
Model Tree
unsloth/Qwen3-14B-unsloth-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
| starting adapter | distilled | this adapter | |
|---|---|---|---|
| changes that came right after an explicit yes (τ-bench) | 44% | 37% | 59% |
| tasks with 2+ orders where all orders were opened before the first change | 18% | 21% | 24% |
| opened an order straight after loading the account | 66% | 59% | 37% |
Confirmation: yes. Lookup: no. The "open every order" decision was only 178 examples (about 1% of the agent text), against about 3,900 unchanged ones, and the model keeps reasoning "which order does the user mean, I should ask". We built a habit-only dataset (only lookup and confirmation turns carry loss, 898 conversations) to fix that; it has not been trained yet.
Lookup (72) is unchanged against the distilled model (75); changing before a yes fell (52 vs 68); wrong or invented values rose (72 vs 52). The model also takes more steps: 27 of 460 τ-bench rollouts hit the 25-step limit (control 7, distilled 10) and 19 of 228 τ³ rollouts hit a 20-minute limit (baseline 1).
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = "unsloth/Qwen3-14B-unsloth-bnb-4bit"model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, "teacher57/qwen3-14b-tau-lookup-confirm-augmented")tok = AutoTokenizer.from_pretrained("teacher57/qwen3-14b-tau-lookup-confirm-augmented")For evaluation we served it with vLLM 0.11.0 (--enable-lora --max-loras 3 --max-lora-rank 16, tool parser hermes, reasoning parser qwen3).
Retail domain only; trained and tested with GPT-4o or GPT-4.1 as the simulated customer; one training run and 2 to 4 trials per task, so differences of a few points are within noise; the confirmation text in training is built from the database and always correct, while at test time the model writes its own and still picks wrong items often.