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LlaMa2-CodeGen

This model is LlaMa2-7b which is fine-tuned on the CodeSearchNet dataset by using the method QLoRA with PEFT library.

Model Trained on Google Colab Pro Using AutoTrain, PEFT and QLoRA

Open in Colab

You can load the LlaMa2-CodeGen model on google colab.

Example



import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

peft_model_id = "AhmedSSoliman/Llama2-CodeGen-PEFT-QLoRA"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)




def create_prompt(instruction):
  system = "You are using the Llam2-CodeGen model, a coding assistant that will help the user to resolve the following instruction:\n"
  instruction = "### Input: " + instruction
  return system + "\n" + instruction + "\n\n" + "### Response:" + "\n"

def generate(
        instruction,
        max_new_tokens=128,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs,
):
    prompt = create_prompt(instruction)
    print(prompt)
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    #input_ids = inputs["input_ids"].to("cuda")
    #attention_mask = inputs["attention_mask"].to("cuda")

    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **kwargs,
    )
    with torch.no_grad():
        generation_output = model.generate(
            #input_ids=input_ids,
            #attention_mask=attention_mask,
            **inputs,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
            early_stopping=True
        )



    generated_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    stop_output = "### Input"
    gen_response = (generated_response.split(stop_output))[0]


    #s = generation_output.sequences[0]
    #output = tokenizer.decode(s, skip_special_tokens=True)
    #stop_output = "### Input"

    #gen_response = (output.split(stop_output))[0]


    #return output.split("### Response:")[1].lstrip("\n")
    return gen_response






instruction = """
 Write a python code for the name Ahmed to be in a reversed order
"""
print(generate(instruction))
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Datasets used to train AhmedSSoliman/Llama2-CodeGen-PEFT-QLoRA