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Nicholas55555
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Model Details
Model Provider
Nicholas55555
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| field | values |
|---|---|
intent | weather, out_of_scope |
location | place name, or null |
date | today, tomorrow, day_after_tomorrow, this_weekend, next_weekend, next_week, a weekday, or null |
metric | temperature, rain, snow, wind, humidity, uv, cloud, general |
time_of_day | morning, afternoon, evening, night, or null |
| metric | base | ft | ft_grammar |
|---|---|---|---|
| in-scope slot exact-match | 52.0% | 99.6% | 100.0% |
| OOS recall (rejects unanswerable) | 80.0% | 100.0% | 100.0% |
| FAR — false-accept / confabulation | 20.0% | 0.0% | 0.0% |
| FRR — false-reject | 20.9% | 0.4% | 0.0% |
| out-of-vocab errors | 30 | 1 | 0 |
The honest read: reject-training does the heavy lifting (ft alone drops false-accept to 0); grammar is the last-mile guarantee that drives out-of-vocab to a structural 0. The gate turns structure into a cheap-over-escalation routing decision. Grammar guarantees validity, not correctness — the constraint tax is measured, not assumed.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-1.5B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, device_map="auto"), "Nicholas55555/qwen2.5-1.5b-weather-intent-v2") sys = "You extract structured intent from user messages about weather. Return ONLY a JSON object..."msgs = [{"role": "system", "content": sys}, {"role": "user", "content": "how's the pollen in Austin?"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)print(tok.decode(model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=64)[0], skip_special_tokens=True))# -> {"intent": "out_of_scope", ...} (pollen is not a supported metric)For guaranteed-valid output, pair with XGrammar constrained decoding against the schema.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen2.5-1.5B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, device_map="auto"), "Nicholas55555/qwen2.5-1.5b-weather-intent-v2") sys = "You extract structured intent from user messages about weather. Return ONLY a JSON object..."msgs = [{"role": "system", "content": sys}, {"role": "user", "content": "how's the pollen in Austin?"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)print(tok.decode(model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=64)[0], skip_special_tokens=True))# -> {"intent": "out_of_scope", ...} (pollen is not a supported metric)