Create README.md

README.md

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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 = text.replace(tokenizer.eos_token,'')
+    print(patch)
+
+
+```