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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- distillation
+- agentic-rag
+- qasper
+- scientific-qa
+- react
+- lora
+datasets:
+- allenai/qasper
+---
+
+# DistillAgent-PaperQA-3B
+
+DistillAgent-PaperQA-3B is a compact agentic QA model distilled from tool-using trajectories for question answering over scientific papers (QASPER).
+
+It is fine-tuned from `Qwen/Qwen2.5-3B-Instruct` using LoRA/rsLoRA with constrained Thought/Action/Observation/Final Answer trajectories.
+
+## Highlights
+
+- Small model with practical agentic behavior on research-paper QA.
+- Outperforms base model in our QASPER 200-sample evaluation.
+
+## Model Details
+
+- Base model: `Qwen/Qwen2.5-3B-Instruct`
+- Training: LoRA / rsLoRA SFT
+- Domain: scientific paper QA (QASPER)
+- Inference style: constrained ReAct + section lookup
+
+## Evaluation Summary (QASPER, 200 samples)
+
+| Model | EM | Mean F1 | Mean hops | Mean latency |
+|---|---:|---:|---:|---:|
+| DistillAgent-PaperQA-3B (SFT) | 14.5% | 0.2425 | 2.36 | 37.28s |
+| Base Qwen2.5-3B-Instruct | 9.0% | 0.1650 | 3.00 | 20.04s |
+
+Notes:
+- Hops and latency depend on runtime harness and hardware.
+- Main quality outcome: SFT > base on EM and F1.
+
+## Intended Use
+
+- QA over scientific/technical papers with section-level lookup or retrieval.
+- Research and educational workflows for compact agentic model distillation.
+
+## Limitations
+
+- Sensitive to runtime prompt/harness format.
+- Multi-hop behavior can increase latency.
+- Should not be used as sole source for high-stakes scientific or medical decisions.
+
+## Usage (Transformers)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+repo_id = "QuantumCuddle/DistillAgent-PaperQA-3B"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "QUESTION: What baseline method is used?\nAVAILABLE PAPER SECTIONS:\n1. Abstract\n2. Methods\n..."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=256, temperature=0.0)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+## Citation
+
+```bibtex
+@misc{distillagent_paperqa_3b_2026,
+  title={DistillAgent-PaperQA-3B},
+  author={QuantumCuddle},
+  year={2026},
+  howpublished={\url{https://huggingface.co/QuantumCuddle/DistillAgent-PaperQA-3B}}
+}
+```