Fine-tuned version of CodeLlama-7b-hf with QLoRA (4-bit NF4 quantization + LoRA rank=8) on CodeSearchNet Python, replicating "Optimizing Python Code Completion with Parameter-Efficient Fine-Tuning".

Model Details

Property	Value
Base model	codellama/CodeLlama-7b-hf
Fine-tuning method	QLoRA (LoRA + 4-bit NF4 quantization)
LoRA rank	8
LoRA alpha	16
LoRA dropout	0.1
Target modules	q_proj, v_proj
Training dataset	CodeSearchNet Python (500-sample subset)
Training hardware	Google Colab T4 (16GB VRAM)
Epochs	3
Optimizer	AdamW
Learning rate	2e-4
Effective batch size	16 (4 × 4 gradient accumulation)

Results

Evaluated on HumanEval benchmark using the official evaluate_functional_correctness scorer with 10 samples per problem.

Metric	Paper (full dataset, BF16)	This adapter (500 samples, QLoRA)
pass@1	37.8%	26.83%
pass@5	58.4%	35.91%
pass@10	66.1%	38.41%

The gap relative to the paper is expected: this adapter was trained on a 500-sample subset due to Colab free-tier constraints, and uses 4-bit quantization instead of full BF16 precision.

Training

Trained using the sedanurkilic/Python-Code-Completion-CodeLlama-7B- codebase. Each CodeSearchNet sample was formatted as:

[INST] {docstring} [/INST]
{function_body}

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

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

tokenizer = AutoTokenizer.from_pretrained("sedaklc/codellama-7b-qlora-humaneval")
tokenizer.pad_token = tokenizer.eos_token

base_model = AutoModelForCausalLM.from_pretrained(
    "codellama/CodeLlama-7b-hf",
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model = PeftModel.from_pretrained(base_model, "sedaklc/codellama-7b-qlora-humaneval")
model.eval()

prompt = "[INST] Return the n-th Fibonacci number. [/INST]\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.2,
        top_p=0.95,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

new_tokens = output[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Limitations

Trained on a 500-sample subset; performance improves significantly with more data.
4-bit quantization introduces a small quality degradation vs full-precision weights.
Optimised for Python function completion from docstrings; not a general-purpose chat model.