Update README.md

README.md

@@ -18,122 +18,128 @@ tags:
 ---
 **model_name: 169Pi/generic_slm**
 
-**model_description**: 
-  The 169Pi/generic_slm is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, 
-  designed to deliver high-quality educational content. Leveraging techniques such as 
-  LoRA, PEFT, and RSLoRA, it aims to provide engaging, accurate, and contextually 
-  appropriate educational materials for students and educators.
-
-**tags**:
-  - transformers
-  - llama
-  - education
-  - fine-tuning
-  - LoRA
-  - PEFT
-  - RSLoRA
-  - quantized
-
-**uses**:
-  direct_use:
-    - Summarizing chapters or concepts
-    - Answering curriculum-aligned questions
-    - Generating practice questions and explanations
-    - Recommending study materials
-    
-  **downstream_use**:
-    - Interactive learning tools
-    - Educational chatbots
-    - Personalized study guides
-    - Automated assessment materials
-    
-  **out_of_scope**:
-    - Legal or financial decision-making
-    - Generating non-educational content
-    - Applications requiring high precision in non-educational contexts
-
-**training_details**:
-  dataset: Proprietary dataset by 169Pi
-  
-  **preprocessing_steps**:
-    - Removed duplicates
-    - Cleaned noisy and irrelevant data
-    - Normalized text for consistency
-    
-  **parameter_size**: 4.65 billion (after quantization to 4-bit)
-  
-  **hyperparameters**:
-    - learning_rate: 5e-5
-    - lr_scheduler_type: cosine
-    - batch_size_per_device: 32
-    - gradient_accumulation_steps: 4
-    - num_epochs: 3
-    - fp16: True
-    - bf16: True
-    - optimizer: adamw_8bit
-    - weight_decay: 0.05
-    - warmup_steps: 1000
-    - logging_steps: 1000
-    - evaluation_strategy: steps
-    - eval_steps: 1000
-    - save_strategy: steps
-    - save_steps: 1000
-    
-
-**architecture**:
-  base_model: Meta-Llama-3.1-8B
-  quantization: 4-bit
-  techniques:
-    - LoRA
-    - PEFT
-    - RSLoRA
-
-**bias_risks_and_limitations**:
-  known_biases: >
-    Potential biases in educational content sources, including cultural or linguistic preferences.
-  risks: >
-    Model may generate incorrect or general responses for ambiguous queries.
-  recommendations: >
-    Use cautiously in critical contexts. Regularly evaluate outputs for accuracy and bias.
-
-**technical_specifications**:
-  model_architecture: >
-    Transformer-based architecture with multi-head self-attention, enhanced using LoRA, 
-    PEFT, and RSLoRA. Optimized for educational tasks.
-    
-  **objective**: >
-    Generate high-quality educational content, including summarization, question-answering, 
-    and study material generation.
-
-**evaluation**:
-  metrics:
-    primary: Loss during training
-    secondary: Accuracy and relevance through manual evaluation
-    
-  **results**: >
-    Achieved low validation loss during training, demonstrating generalization capability.
-
-**environmental_impact**:
-  hardware: NVIDIA A100
-  
-  **training_duration**: 26 hours
-  
-
-**citation**: >
-  @misc{169Pi_generic_slm,
-  
-    title={169Pi/generic_slm: Fine-Tuned Educational Model},
-    
-    author={169Pi},
-    
-    year={2024},
-    
-    publisher={Hugging Face},
-    
-    url={https://huggingface.co/169Pi/generic_slm}
-  }
-  
-**contact**:
-  developer: 169Pi AI
-  
-  email: contact@169pi.com
+## Model Description
+
+The **169Pi/generic_slm** is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, designed to deliver high-quality educational content. Leveraging techniques such as LoRA, PEFT, and RSLoRA, it aims to provide engaging, accurate, and contextually appropriate educational materials for students and educators.
+
+## Tags
+
+- transformers
+- llama
+- education
+- fine-tuning
+- LoRA
+- PEFT
+- RSLoRA
+- quantized
+
+## Uses
+
+### Direct Use
+- Summarizing chapters or concepts
+- Answering curriculum-aligned questions
+- Generating practice questions and explanations
+- Recommending study materials
+
+### Downstream Use
+- Interactive learning tools
+- Educational chatbots
+- Personalized study guides
+- Automated assessment materials
+
+### Out of Scope
+- Legal or financial decision-making
+- Generating non-educational content
+- Applications requiring high precision in non-educational contexts
+
+## Training Details
+
+### Dataset
+Proprietary dataset by 169Pi
+
+### Preprocessing Steps
+- Removed duplicates
+- Cleaned noisy and irrelevant data
+- Normalized text for consistency
+
+### Parameter Size
+4.65 billion (quantized to 4-bit)
+
+### Hyperparameters
+- **learning_rate**: 5e-5
+- **lr_scheduler_type**: cosine
+- **batch_size_per_device**: 32
+- **gradient_accumulation_steps**: 4
+- **num_epochs**: 3
+- **fp16**: True
+- **bf16**: True
+- **optimizer**: adamw_8bit
+- **weight_decay**: 0.05
+- **warmup_steps**: 1000
+- **logging_steps**: 1000
+- **evaluation_strategy**: steps
+- **eval_steps**: 1000
+- **save_strategy**: steps
+- **save_steps**: 1000
+
+## Architecture
+
+### Base Model
+Meta-Llama-3.1-8B
+
+### Quantization
+4-bit
+
+### Techniques
+- LoRA
+- PEFT
+- RSLoRA
+
+## Bias, Risks, and Limitations
+
+### Known Biases
+Potential biases in educational content sources, including cultural or linguistic preferences.
+
+### Risks
+Model may generate incorrect or general responses for ambiguous queries.
+
+### Recommendations
+Use cautiously in critical contexts. Regularly evaluate outputs for accuracy and bias.
+
+## Technical Specifications
+
+### Model Architecture
+Transformer-based architecture with multi-head self-attention, enhanced using LoRA, PEFT, and RSLoRA. Optimized for educational tasks.
+
+### Objective
+Generate high-quality educational content, including summarization, question-answering, and study material generation.
+
+## Evaluation
+
+### Metrics
+- **Primary**: Loss during training
+- **Secondary**: Accuracy and relevance through manual evaluation
+
+### Results
+Achieved low validation loss during training, demonstrating generalization capability.
+
+## Environmental Impact
+
+- **Hardware**: NVIDIA A100
+- **Training Duration**: 26 hours
+
+## Citation
+
+```bibtex
+@misc{169Pi_generic_slm,
+  title={169Pi/generic_slm: Fine-Tuned Educational Model},
+  author={169Pi},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/169Pi/generic_slm}
+}
+
+## Contact
+
+- **Developer**: 169Pi AI  
+- **Email**: [[email protected]](mailto:[email protected])