Improve README

README.md

@@ -44,6 +44,65 @@ It could be useful for:
 - 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.