Update README.md

README.md

@@ -26,13 +26,63 @@ This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslot
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 How to use
-This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original llama codebase.
+This repository contains two versions of Gemma-1-9B, for use with transformers and with the original llama codebase.
 
 Use with transformers
 Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
 
 Make sure to update your transformers installation via pip install --upgrade transformers.
 
+You need to prepare prompt in alpaca format to generate properly:
+```python
+def format_test(x):
+
+  if x['input']:
+    formatted_text = f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+
+    ### Instruction:
+    {x['instruction']}
+
+    ### Input:
+    {x['input']}
+
+    ### Response:
+    """
+
+  else:
+    formatted_text = f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+
+    ### Instruction:
+    {x['instruction']}
+
+    ### Response:
+    """
+
+  return formatted_text
+
+# using code_instructions_122k_alpaca dataset
+Prompt = format_test(data[155])
+print(Prompt)
+
+```
+- transfomer method:
+```python
+from transformers import TextStreamer
+
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    Prompt
+], return_tensors = "pt").to("cuda")
+
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)
+```
+
+
+- unsloth method 
 ```python
 from unsloth import FastLanguageModel