---
license: apache-2.0
tags:
- unsloth
- query-expansion
datasets:
- s-emanuilov/query-expansion
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
# Query Expansion Dataset - based on Qwen2.5-3B

Fine-tuned Qwen2.5-3B model for generating search query expansions.   
Part of a collection of query expansion models available in different architectures and sizes.

## Overview

**Task:** Search query expansion  
**Base model:** [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B)  
**Training data:** [Query Expansion Dataset](https://huggingface.co/datasets/s-emanuilov/query-expansion)  

<img src="static/query-expansion-model.jpg" alt="Query Expansion Model" width="600px" />


## Variants
### Fine-tuned models

- [Qwen2.5-7B](https://huggingface.co/s-emanuilov/query-expansion-Qwen2.5-7B)
- [Llama-3.2-3B](https://huggingface.co/s-emanuilov/query-expansion-Llama-3.2-3B)

### GGUF variants
- [Qwen2.5-3B-GGUF](https://huggingface.co/s-emanuilov/query-expansion-Qwen2.5-3B-GGUF)
- [Qwen2.5-7B-GGUF](https://huggingface.co/s-emanuilov/query-expansion-Qwen2.5-7B-GGUF)
- [Llama-3.2-3B-GGUF](https://huggingface.co/s-emanuilov/query-expansion-Llama-3.2-3B-GGUF)

Each GGUF model is available in several quantization formats: F16, Q8_0, Q5_K_M, Q4_K_M, Q3_K_M

## Details
This model is designed for enhancing search and retrieval systems by generating semantically relevant query expansions.

It could be useful for:
- Advanced RAG systems
- Search enhancement
- Query preprocessing
- Low-latency query expansion

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from unsloth import FastLanguageModel

# Model configuration
MODEL_NAME = "s-emanuilov/query-expansion-Qwen2.5-3B"
MAX_SEQ_LENGTH = 2048
DTYPE = "float16" 
LOAD_IN_4BIT = True

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=MODEL_NAME,
    max_seq_length=MAX_SEQ_LENGTH,
    dtype=DTYPE,
    load_in_4bit=LOAD_IN_4BIT,
)

# Enable faster inference
FastLanguageModel.for_inference(model)

# Define prompt template
PROMPT_TEMPLATE = """Below is a search query. Generate relevant expansions and related terms that would help broaden and enhance the search results.

### Query:
{query}

### Expansions:
{output}"""

# Prepare input
query = "apple stock"
inputs = tokenizer(
    [PROMPT_TEMPLATE.format(query=query, output="")],
    return_tensors="pt"
).to("cuda")

# Generate with streaming output
from transformers import TextStreamer
streamer = TextStreamer(tokenizer)
output = model.generate(
    **inputs,
    streamer=streamer,
    max_new_tokens=128,
)
```

## Example

**Input:** "apple stock"
**Expansions:**
- "apple stock price"
- "how to invest in apple stocks"
- "apple stock analysis"
- "what is the future of apple stocks?"
- "understanding apple's stock market performance"

## Citation

If you find my work helpful, feel free to give me a citation.

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```