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---
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)
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