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README.md

@@ -19,4 +19,58 @@ tags:
 
 This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
+
+### Usage 
+
+```python
+
+from transformers import TextStreamer
+
+alpaca_prompt = """Below are the tools that you have access to these tools. Use them if required.
+
+### Tools:
+{}
+
+### Query:
+{}
+
+### Response:
+{}"""
+
+tools = [
+    {
+        "name": "upcoming",
+        "description": "Fetches upcoming CS:GO matches data from the specified API endpoint.",
+        "parameters": {
+            "content_type": {
+                "description": "The content type for the request, default is 'application/json'.",
+                "type": "str",
+                "default": "application/json",
+            },
+            "page": {
+                "description": "The page number to retrieve, default is 1.",
+                "type": "int",
+                "default": "1",
+            },
+            "limit": {
+                "description": "The number of matches to retrieve per page, default is 10.",
+                "type": "int",
+                "default": "10",
+            },
+        },
+    }
+]
+query = """Can you fetch the upcoming CS:GO matches for page 1 with a 'text/xml' content type  and a limit of 20 matches? Also, can you fetch the upcoming matches for page 2 with the 'application/xml' content type and a limit of 15 matches?"""
+
+FastLanguageModel.for_inference(model)
+
+model_input = tokenizer(alpaca_prompt.format(tools, query, ""), return_tensors="pt")
+
+output = model.generate(**input, max_new_tokens=1024, temperature = 0.0)
+
+decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+
+
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)