teckmill/jaleah-ai-model

Jaleah AI Code Generation Model

Model Description

Jaleah AI is a fine-tuned version of the Microsoft CodeGPT small Python model, specialized in generating high-quality Python code snippets across various domains.

Model Details

• Developed by: TeckMill AI Research Team
• Base Model: microsoft/CodeGPT-small-py
• Language: Python
• Version: 1.0

Jaleah AI Code Generation Model

Model Description

Jaleah AI is a fine-tuned version of the Microsoft CodeGPT small Python model, specialized in generating high-quality Python code snippets across various domains.

Model Details

• Developed by: TeckMill AI Research Team
• Base Model: microsoft/CodeGPT-small-py
• Language: Python
• Version: 1.0

Jaleah AI Code Generation Model

Model Description

Jaleah AI is a fine-tuned version of the Microsoft CodeGPT small Python model, specialized in generating high-quality Python code snippets across various domains.

Model Details

• Developed by: TeckMill AI Research Team
• Base Model: microsoft/CodeGPT-small-py
• Language: Python
• Version: 1.0

Intended Uses & Limitations

Intended Uses

• Code snippet generation
• Assisting developers with Python programming
• Providing intelligent code suggestions
• Rapid prototyping of Python functions and classes

Limitations

• May generate syntactically incorrect code
• Requires human review and validation
• Performance may vary across different coding domains
• Not suitable for complete project generation

Training Data

Data Sources

The model was trained on a diverse dataset including:

• GitHub trending repositories
• Stack Overflow top-rated code answers
• Open-source Python project codebases
• Synthetic code generation
• Complex algorithmic implementations

Data Preprocessing

• Syntax validation
• Comment and docstring removal
• Length and complexity filtering

Training Procedure

Training Hyperparameters

• Learning Rate: 5e-05
• Batch Size: 4
• Epochs: 12
• Optimizer: AdamW
• Learning Rate Scheduler: Linear
• Weight Decay: 0.01

Training Process

• Fine-tuning of pre-trained CodeGPT model
• Multi-source code collection
• Advanced synthetic code generation
• Rigorous code validation

Evaluation

Detailed evaluation metrics to be added in future versions.

Ethical Considerations

• Designed to assist, not replace, human developers
• Encourages learning and code understanding

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teckmill/jaleah-ai-model")
tokenizer = AutoTokenizer.from_pretrained("teckmill/jaleah-ai-model")

def generate_code(prompt, max_length=200):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
    return tokenizer.decode(output[0], skip_special_tokens=True)