Update README

README.md

@@ -2,4 +2,111 @@
 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 Qwen2.5-7B
+
+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:** [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-7B)  
+**Training data:** [Query Expansion Dataset](https://huggingface.co/datasets/unsloth/query-expansion-dataset)  
+
+<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)
+- [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-Qwen2.5-7B"
+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:**
+- "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.
+
+```
+
+```