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---
license: llama3.2
tags:
- unsloth
- query-expansion
datasets:
- s-emanuilov/query-expansion
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# Query Expansion Dataset - based on Llama-3.2-3B
Fine-tuned Llama-3.2-3B 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:** [Llama-3.2-3B-Instruct](https://huggingface.co./meta-llama/Llama-3.2-3B-Instruct)
**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
### LoRA adaptors
- [Qwen2.5-3B](https://huggingface.co./s-emanuilov/query-expansion-Qwen2.5-3B)
- [Qwen2.5-7B](https://huggingface.co./s-emanuilov/query-expansion-Qwen2.5-7B)
### 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-Llama-3.2-3B"
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 market"
- "apple news"
- "apple stock price"
- "apple stock forecast"
## Citation
If you find my work helpful, feel free to give me a citation.
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``` |