Upload README.md

README.md

@@ -19,10 +19,10 @@ We've fine-tuned Gemma-2b with an additional 0.7 billion high-quality, code-rela
 
 ### Usage
 
-Here give an example of how to use our model.
+Here give some examples of how to use our model:
 
 ```python
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 PROMPT = """### Instruction
 {instruction}
@@ -30,6 +30,29 @@ PROMPT = """### Instruction
 """
 instruction = <Your code instruction here>
 prompt = PROMPT.format(instruction=instruction)
+tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CodeGemma-2b")
+model = AutoModelForCausalLM.from_pretrained(
+    "TechxGenus/CodeGemma-2b",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+inputs = tokenizer.encode(prompt, return_tensors="pt")
+outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=2048)
+print(tokenizer.decode(outputs[0]))
+```
+
+With text-generation pipeline:
+
+
+```python
+from transformers import pipeline
+import torch
+PROMPT = """<bos>### Instruction
+{instruction}
+### Response
+"""
+instruction = <Your code instruction here>
+prompt = PROMPT.format(instruction=instruction)
 generator = pipeline(
     model="TechxGenus/CodeGemma-2b",
     task="text-generation",