Update README.md

README.md

@@ -21,7 +21,7 @@ with permissive licenses, namely MIT and Apache 2.0. This set of code was furthe
 For our fine-tuning, we decided to follow a 2-step strategy.
 - Pretraining (Fine-tuning) with next token prediction on the previously built gradio dataset (this step should familiarize the model with the gradio syntax.).
 - Instruction fine-tuning on an instruction dataset (this step should make the model conversational.).
-For both steps, we made use of parameter-efficient fine-tuning via the library [PEFT](https://github.com/huggingface/peft), more precisely [LoRa](https://arxiv.org/abs/2106.09685). Our
+For both steps, we made use of parameter-efficient fine-tuning via the library [PEFT](https://github.com/huggingface/peft), more precisely [LoRA](https://arxiv.org/abs/2106.09685). Our
 training script is the famous [starcoder fine-tuning script](https://github.com/bigcode-project/starcoder).
 
 ## Resources
@@ -59,8 +59,10 @@ model = AutoModelForCausalLM.from_pretrained(checkpoint_name)
 tokenizer = AutoTokenizer.from_pretrained(checkpoint_name)
 prompt = "Create a gradio application that help to convert temperature in celcius into temperature in Fahrenheit"
 inputs = tokenizer(f"Question: {prompt}\n\nAnswer: ", return_tensors="pt")
-outputs = model.generate(inputs["input_ids"], temperature=0.2, top_p=0.95)
-print(tokenizer.decode(outputs))
+outputs = model.generate(inputs["input_ids"], temperature=0.2, top_p=0.95, max_length=200)
+input_len=len(inputs["input_ids"])
+print(tokenizer.decode(outputs[0][input_len:]))
 ```
+
 # More information
 For further information, refer to [StarCoder](https://huggingface.co/bigcode/starcoder).