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Model: HaimingW/qwen2.5-1.5b-faithful-summarization
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-1.5B
+library_name: transformers
+---
+
+# Qwen2.5-1.5B Faithful Summarization SFT
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) for faithful document summarization.
+
+## Model Details
+
+- **Developed by:** HaimingW
+- **Model type:** Causal Language Model (Fine-tuned)
+- **Language(s):** English
+- **License:** Same as base model (Qwen License)
+- **Finetuned from model:** Qwen/Qwen2.5-1.5B
+
+## Training Details
+
+### Training Data
+- Decontaminated public summarization datasets:
+  - XSum
+  - CNN/DailyMail
+  - BillSum
+- Decontaminated against held-out evaluation set using exact substring + n-gram overlap filtering
+- 35,659 training examples after filtering
+
+### Training Hyperparameters
+- **Method:** LoRA SFT (r=32, alpha=64, all linear layers) with merged full-weight checkpoint
+- **Epochs:** 1
+- **Learning rate:** 2e-4 with cosine decay
+- **Batch size:** 1 per device, gradient accumulation 16 (effective batch 32)
+- **Max sequence length:** 2048
+- **Precision:** bf16
+- **Hardware:** 2x NVIDIA H20
+
+## Evaluation
+
+Evaluated on 360 held-out summarization items:
+- **Faithfulness:** 0.547
+- **Coverage:** 0.422
+- **Combined score:** 0.476 (target: 0.45)
+- **Degenerate fraction:** 11.9%
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("HaimingW/qwen2.5-1.5b-faithful-summarization", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("HaimingW/qwen2.5-1.5b-faithful-summarization")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant that summarizes documents faithfully."},
+    {"role": "user", "content": "Summarize the following document:\n\n<document text here>"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=128)
+summary = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+```
+
+## Citation
+
+Please cite the base model and datasets:
+- Qwen2.5: [Qwen team](https://huggingface.co/Qwen)
+- XSum, CNN/DailyMail, BillSum datasets