metadata

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
      top_k:
        default: 50
        range:
          - 1
          - 100
      top_p:
        default: 0.95
        range:
          - 0.1
          - 1
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

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)