---
language: 
- code
tags:
- code-generation
- ai-assistant
- code-completion
- python
- machine-learning
- transformer
- gpt
license: mit
datasets:
- github-code
- stackoverflow
- synthetic-code
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Jaleah AI Code Generator
  results:
  - task: 
      type: text-generation
      name: Code Generation
    dataset:
      name: Multi-Source Python Code Corpus
      type: mixed
    metrics:
      - type: code-generation
        name: Code Generation Score
        value: experimental
      - type: syntax-correctness
        name: Syntax Correctness Rate
        value: high
      - type: contextual-relevance
        name: Contextual Relevance
        value: moderate
  parameters:
    max_length: 
      default: 200
      range: 
        - 50
        - 500
    temperature:
      default: 0.7
      range: 
        - 0.1
        - 1.0
    top_k:
      default: 50
      range: 
        - 1
        - 100
    top_p:
      default: 0.95
      range: 
        - 0.1
        - 1.0
model_type: causal
architectures:
- GPTNeoForCausalLM
training_config:
  base_model: microsoft/CodeGPT-small-py
  training_objective: causal-language-modeling
  compute_environment: 
    - gpu
    - cloud
  training_time: ~3 hours
  hardware: 
    - cuda
    - t4-gpu
---





# 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
```python
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)