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CodeLlama-70B_for_NMT

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CodeLlama-70B_for_NMT
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# CodeLlama-70B_for_NMT

We fine-tuned [CodeLlama-70B](https://huggingface.co/codellama/CodeLlama-70b-hf) on [Transfer_dataset](https://drive.google.com/drive/folders/1Z-2xcLSmh643BfX_j0yQW2GmdPoru6j3?usp=drive_link) under the NMT workflow for APR research.

## Model Use

To use this model, please make sure to install transformers, peft, bitsandbytes, and accelerate.

```bash
pip install transformers
pip install peft
pip install bitsandbytes
pip install accelerate
```

Then, please run the following script to merge the adapter into the CodeLlama.

```bash
bash merge.sh
```

Finally, you can load the model to generate patches for buggy code.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
import torch


# load model and tokenizer

tokenizer = AutoTokenizer.from_pretrained("CodeLlama-70B_for_NMT/Epoch_1/-merged", use_auth_token=True)

nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(
    "CodeLlama-70B_for_NMT/Epoch_1/-merged",
    quantization_config=nf4_config,
    device_map='auto'
)    

model = prepare_model_for_kbit_training(model)

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
)

model = get_peft_model(model, lora_config)


# a bug-fix pairs

buggy_code = "
/*
 * Evaluate whether the given number n can be written as the sum of exactly 4 positive even numbers
    Example
    is_equal_to_sum_even(4) == False
    is_equal_to_sum_even(6) == False
    is_equal_to_sum_even(8) == True
 */
public class IS_EQUAL_TO_SUM_EVEN {
    public static boolean is_equal_to_sum_even(int n) {
// bug_start
        return ((n * 2 == 1) ^ (n < 8));
// bug_end
    }
}
"

fixed_code = "
// fix_start
        return ((n % 2 == 0) && (n >= 8));
// fix_end
"

# model inference

B_INST, E_INST = "[INST]", "[/INST]"
input_text =  tokenizer.bos_token + B_INST +'\n[bug_function]\n' + buggy_code + '\n[fix_code]\n' + E_INST
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)

eos_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
generated_ids = model.generate(
    input_ids=input_ids,
    max_new_tokens=256,
    num_beams=10,
    num_return_sequences=10,
    early_stopping=True,
    pad_token_id=eos_id,
    eos_token_id=eos_id
)

for generated_id in generated_ids:
    generated_text = tokenizer.decode(generated_id, skip_special_tokens=False)
    patch = generated_text.split(E_INST)[1]
    patch = patch.replace(tokenizer.eos_token,'')
    print(patch)


```

## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).