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