license, base_model, tags, pipeline_tag

license	base_model	tags	pipeline_tag
apache-2.0	Qwen/Qwen2.5-1.5B-Instruct	qwen2 text-generation structured-output intent-parsing merged	text-generation

qwen2.5-1.5b-weather-intent-v2-merged

Standalone merged model: Qwen/Qwen2.5-1.5B-Instruct + the weather-intent LoRA adapter, merged to fp16 so it can be quantized to GGUF (llama.cpp / Ollama) or served directly. Parses a natural-language weather question into a compact structured intent (JSON).

• Adapter: Nicholas55555/qwen2.5-1.5b-weather-intent-v2
• Dataset: Nicholas55555/weather-intent-v2

Results (held-out eval)

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

Base = few-shot; fine-tuned = zero-shot. Greedy decoding, identical prompt.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Nicholas55555/qwen2.5-1.5b-weather-intent-v2-merged")
model = AutoModelForCausalLM.from_pretrained("Nicholas55555/qwen2.5-1.5b-weather-intent-v2-merged", device_map="auto")

sys = "You extract structured intent from weather questions. Return ONLY a JSON object..."
msgs = [{"role": "system", "content": sys},
        {"role": "user", "content": "will it rain in Paris this weekend?"}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))