This model is a fine-tuned version of 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:

Reading provided source documents
Generating accurate, concise answers
Citing sources using [1], [2], [3] format
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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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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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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Framework versions

PEFT 0.17.1