Update README.md

README.md

@@ -38,6 +38,34 @@ state of the art AI models and helping foster innovation for everyone.
 
 Google/Gemma has shared some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
 
+    hf_model_repo = "Geerath/Google_gemma_web_questions"
+    
+    # Get the tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(hf_model_repo)
+    
+    # Load the model
+    
+    
+    model = AutoModelForCausalLM.from_pretrained(hf_model_repo,
+                                                 quantization_config=bnb_config,
+                                                 device_map="auto")
+    
+    prompt = ["Question: Tell me something about IISc\n\nAnswer:\n"]
+    
+    # Generate response
+    %%time
+    input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids
+    outputs = model.generate(input_ids=input_ids,
+                             max_new_tokens=200,
+                             do_sample = True,
+                             temperature=0.2)
+    
+    result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    
+    result = "Question:"+result.split("Question:")[1]
+
+# Print the result
+print(f"Generated response:\n{result}")
 #### Fine-tuning the model
 
 You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-7b-it`.