Update README.md

README.md

@@ -1,52 +1,128 @@
 ---
-library_name: transformers
-base_model: microsoft/CodeGPT-small-py
+language:
+  - code
 tags:
-- generated_from_trainer
+  - code-generation
+  - ai-assistant
+  - code-completion
+  - python
+license: mit
+datasets:
+  - github-code
+  - stackoverflow
 model-index:
-- name: jaleah-ai-model
-  results: []
+  - name: Jaleah AI Code Generator
+    results:
+      - task: 
+          type: text-generation
+          name: Code Generation
+        dataset:
+          name: Python Code Corpus
+          type: generated
+        metrics:
+          - type: BLEU
+            value: experimental
+          - type: CodeBLEU
+            value: experimental
+          - type: perplexity
+            value: experimental
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# jaleah-ai-model
 
-This model is a fine-tuned version of [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) on an unknown dataset.
+# Jaleah AI Code Generation Model
 
-## Model description
+## 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.
 
-More information needed
+### Model Details
+- **Developed by:** TeckMill AI Research Team
+- **Base Model:** microsoft/CodeGPT-small-py
+- **Language:** Python
+- **Version:** 1.0
 
-## Intended uses & limitations
+# Jaleah AI Code Generation Model
 
-More information needed
+## 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.
 
-## Training and evaluation data
+### Model Details
+- **Developed by:** TeckMill AI Research Team
+- **Base Model:** microsoft/CodeGPT-small-py
+- **Language:** Python
+- **Version:** 1.0
 
-More information needed
+# Jaleah AI Code Generation Model
 
-## Training procedure
+## 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.
 
-### Training hyperparameters
+### Model Details
+- **Developed by:** TeckMill AI Research Team
+- **Base Model:** microsoft/CodeGPT-small-py
+- **Language:** Python
+- **Version:** 1.0
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 5
+## Intended Uses & Limitations
 
-### Training results
+### 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
 
-### Framework versions
+### 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
 
-- Transformers 4.47.1
-- Pytorch 2.5.1+cu121
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+### 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)