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Model: Nicholas55555/qwen2.5-1.5b-weather-intent-merged
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- qwen2
+- text-generation
+- structured-output
+- intent-parsing
+- merged
+pipeline_tag: text-generation
+---
+# qwen2.5-1.5b-weather-intent-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](https://huggingface.co/Nicholas55555/qwen2.5-1.5b-weather-intent)
+- Dataset: [Nicholas55555/weather-intent](https://huggingface.co/datasets/Nicholas55555/weather-intent)
+
+## Results (held-out eval)
+
+| metric | base | finetuned |
+|---|---|---|
+| valid JSON | 100.0% | 100.0% |
+| exact match | 64.5% | 98.6% |
+| field accuracy | 90.7% | 99.7% |
+| slot F1 | 0.894 | 0.996 |
+
+Base = few-shot; fine-tuned = zero-shot. Greedy decoding, identical prompt.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Nicholas55555/qwen2.5-1.5b-weather-intent-merged")
+model = AutoModelForCausalLM.from_pretrained("Nicholas55555/qwen2.5-1.5b-weather-intent-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))
+```