s-emanuilov/query-expansion-Qwen2.5-7B

Query Expansion Dataset - based on Qwen2.5-7B

Fine-tuned Qwen2.5-7B 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: Qwen2.5-7B
Training data: Query Expansion Dataset

Variants

Fine-tuned models

• Qwen2.5-3B
• Llama-3.2-3B

GGUF variants

• Qwen2.5-3B-GGUF
• Qwen2.5-7B-GGUF
• 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

from transformers import AutoModelForCausalLM, AutoTokenizer
from unsloth import FastLanguageModel

# Model configuration
MODEL_NAME = "s-emanuilov/query-expansion-Qwen2.5-7B"
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:

• "current apple share value"
• "latest updates on apple's market position"
• "how is apple performing in the current market?"
• "what is the latest information on apple's financial standing?"

Citation

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