Code Assistant with CodeLlama v4

A specialized code assistant model based on CodeLlama-7b-hf, finetuned for code-related tasks including completion, generation, refactoring, and documentation.

Overview

This skill enables AI agents to leverage the Arko007/my-awesome-code-assistant-v4 model for various coding tasks. The model is a fourth-iteration finetuning of CodeLlama-7b-hf using LoRA (Low-Rank Adaptation), optimized for Python, C++, Java, and JavaScript.

Capabilities

Instructions

When using this code assistant model, follow these steps:

1. Installation

First, ensure the required dependencies are installed:

```bash

pip install transformers peft torch

```

2. Load the Model

Load both the base CodeLlama model and the finetuned adapter:

```python

from transformers import AutoModelForCausalLM, AutoTokenizer

from peft import PeftModel

import torch

model_name = "codellama/CodeLlama-7b-hf"

adapter_name = "Arko007/my-awesome-code-assistant-v4"

tokenizer = AutoTokenizer.from_pretrained(model_name)

base_model = AutoModelForCausalLM.from_pretrained(model_name)

model = PeftModel.from_pretrained(base_model, adapter_name)

```

3. Generate Code

Use the model to complete or generate code based on prompts:

```python

prompt = "def factorial(n):"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)

```

4. Prompt Engineering Tips

5. Code Review Requirements

**CRITICAL**: All generated code must be:

Performance Metrics

Limitations

1. **Hallucinations**: May generate plausible but incorrect code

2. **Security Risks**: Generated code may contain vulnerabilities

3. **License Compliance**: Training data includes code with varying licenses

4. **Language Bias**: Performs best on Python, less optimal for other languages

5. **No Factual Guarantees**: Requires human verification for correctness

Example Use Cases

Code Completion

```python

prompt = "def merge_sort(arr):\n # Implementation of merge sort algorithm\n"

Model completes the implementation

```

Function Generation

```python

prompt = "Write a Python function that calculates the Fibonacci sequence up to n terms"

Model generates complete function

```

Code Documentation

```python

prompt = "def complex_algorithm(data, threshold=0.5):\n # TODO: Add docstring"

Model generates comprehensive docstring

```

Refactoring Suggestions

```python

prompt = "# Refactor this code to be more pythonic:\nfor i in range(len(items)):\n print(items[i])"

Model suggests improved implementation

```

Resource Requirements

Out of Scope

Do NOT use this model for:

Citation

```bibtex

@misc{Arko007_my-awesome-code-assistant-v4,

author = {Arko007},

title = {my-awesome-code-assistant-v4},

year = {2024},

publisher = {Hugging Face},

url = {https://huggingface.co/Arko007/my-awesome-code-assistant-v4}

}

```

Additional Resources