---
base_model: Qwen/Qwen2.5-1.5B
library_name: peft
pipeline_tag: text-generation
tags:
- qwen2.5
- lora
- grounded-text-generation
- citation
- transformers
---

# Qwen 2.5-1.5B Fine-tuned for Grounded Text Generation with Citations

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) using LoRA adapters.
It has been trained to generate accurate answers with proper source citations based on provided documents.

## Model Details

### Model Description

This model generates answers to questions by:
1. Reading provided source documents
2. Generating accurate, concise answers
3. Citing sources using [1], [2], [3] format
4. Only using information from the provided documents

- **Developed by:** sungmineom
- **Model type:** Causal Language Model (Fine-tuned with LoRA)
- **Language(s):** English (primary), Korean
- **License:** Same as base model (Qwen2.5-1.5B)
- **Finetuned from model:** Qwen/Qwen2.5-1.5B

### Training Details

- **Training data:** combined_train.json (10,000 samples)
- **Validation data:** combined_test.json (1,000 samples)
- **LoRA rank:** 16
- **LoRA alpha:** 32
- **Batch size:** 2 (with gradient accumulation steps: 8)
- **Learning rate:** 2e-4
- **Epochs:** 3
- **Max sequence length:** 2048 tokens
- **Quantization:** 4-bit (nf4) for training efficiency

## Uses

### Direct Use

This model is designed for Question Answering tasks where you want:
- Accurate answers based on specific documents
- Proper source attribution with citations
- Grounded generation (no hallucination from outside sources)

### Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model
base_model_name = "Qwen/Qwen2.5-1.5B"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "sungmineom/qwen-1.5b-grounded-lora")
tokenizer = AutoTokenizer.from_pretrained("sungmineom/qwen-1.5b-grounded-lora")

# Prepare input
question = "What are the benefits of exercise?"
docs = [
    {"title": "Health Benefits", "text": "Exercise improves cardiovascular health..."},
    {"title": "Mental Health", "text": "Exercise reduces anxiety and depression..."}
]

doc_text = ""
for i, doc in enumerate(docs, 1):
    doc_text += f"Document [{i}](Title: {doc['title']}): {doc['text']}\n"

prompt = f"""Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results (some of which might be irrelevant) and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim. When citing several search results, use [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents.

Question: {question}

{doc_text}
Answer:"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True).split("Answer:")[-1].strip()

print(answer)
```

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.17.1