Update README.md

README.md

@@ -1,141 +1,126 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
-pipeline_tag: text-generation
-tags:
-- llama
-- education
-- transformers
-- fine-tuning
-- LoRA
-- PEFT
-- RSLoRA
-- quantized
----
----
-**model_name: generic_slm**
+# 169Pi/generic_slm
 
 ## Overview
 
-The **generic_slm** is a fine-tuned version of the **Meta-Llama-3.1-8B-bnb-4bit** model, optimized to generate high-quality educational content. This model is designed for tasks such as summarization, question answering, and personalized study material creation. By leveraging techniques like **LoRA**, **PEFT**, and **RSLoRA**, the model delivers contextually accurate and engaging outputs tailored for students and educators.
+The **169Pi/generic_slm** is a state-of-the-art fine-tuned model derived from **Meta-Llama-3.1-8B-bnb-4bit**, purpose-built to deliver high-quality educational content. Designed to meet the needs of students and educators, this model leverages advanced techniques, including **LoRA**, **PEFT**, and **RSLoRA**, to generate accurate, contextually relevant, and engaging outputs. 
+
+This model supports a wide range of educational applications, from summarization to personalized study guide generation, and has been optimized for efficiency with 4-bit quantization.
+
+---
 
 ## Key Features
-- **Model Architecture**: Transformer-based, quantized to 4-bit for efficiency.
-- **Training Optimizations**: Fine-tuned using LoRA, PEFT, and RSLoRA.
-- **Target Audience**: Students, educators, and developers of educational tools.
-- **Applications**: Summarization, curriculum-aligned Q&A, and study guide creation.
-
-## 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
+- **Base Model**: Meta-Llama-3.1-8B, optimized with LoRA, PEFT, and RSLoRA techniques.
+- **Parameter Efficiency**: Quantized to 4-bit for improved performance.
+- **Target Audience**: Students, educators, and developers of educational technology.
+- **Applications**: Summarization, curriculum-aligned Q&A, practice question generation, and more.
+
+---
+
+## Use Cases
+
+### Direct Applications
+- **Concept Summarization**: Generate concise and accurate summaries of academic material.
+- **Curriculum-Aligned Q&A**: Deliver precise answers to subject-specific questions.
+- **Practice Material Creation**: Develop quizzes, questions, and explanations.
+- **Study Resource Recommendations**: Suggest tailored learning resources.
+
+### Downstream Applications
+- **Interactive Learning Platforms**: Enhance user engagement with dynamic educational content.
+- **Educational Chatbots**: Provide on-demand academic assistance.
+- **Personalized Study Guides**: Create customized study materials for individual learners.
+- **Automated Assessment Tools**: Generate and evaluate educational content programmatically.
+
+### Out-of-Scope Applications
+- **Legal or Financial Decision-Making**: This model is not suited for applications outside educational contexts.
+- **Non-Educational Content Generation**: Avoid using the model for tasks unrelated to education.
+- **High-Precision Non-Educational Use Cases**: The model may not deliver the required precision outside its intended domain.
+
+---
 
 ## Training Details
 
 ### Dataset
-Proprietary dataset by 169Pi
+- **Source**: Proprietary educational dataset curated by 169Pi.
+- **Preprocessing Steps**:
+  - Deduplication of redundant data.
+  - Removal of noisy and irrelevant information.
+  - Text normalization for enhanced consistency.
 
-### Preprocessing Steps
-- Removed duplicates
-- Cleaned noisy and irrelevant data
-- Normalized text for consistency
-
-### Parameter Size
-4.65 billion (after quantization to 4-bit)
+### Model Configuration
+- **Parameter Size**: 4.65 billion parameters (quantized to 4-bit).
+- **Hardware Utilized**: NVIDIA A100 GPUs.
+- **Training Duration**: 26 hours.
 
 ### 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
+- **Learning Rate**: `5e-5`
+- **Scheduler**: Cosine
+- **Batch Size**: 32 per device
+- **Gradient Accumulation Steps**: 4
+- **Epochs**: 3
+- **Mixed Precision**: FP16 and BF16
+- **Optimizer**: AdamW (8-bit)
+- **Weight Decay**: 0.05
+- **Warmup Steps**: 1000
+- **Logging Frequency**: Every 1000 steps
+- **Evaluation Strategy**: Per 1000 steps
+- **Model Checkpoints**: Saved every 1000 steps
+
+---
+
+## Technical Specifications
+
+- **Base Model**: Meta-Llama-3.1-8B
+- **Quantization**: 4-bit quantization for computational efficiency.
+- **Fine-Tuning Techniques**:
+  - **LoRA**: Low-Rank Adaptation for parameter-efficient fine-tuning.
+  - **PEFT**: Parameter-Efficient Fine-Tuning.
+  - **RSLoRA**: Residual Scaling with LoRA for enhanced generalization.
+
+### Model Objective
+To generate high-quality educational content tailored for diverse academic needs, including:
+- Topic Summarization
+- Question-Answer Generation
+- Personalized Study Material Creation
+
+---
+
+## Biases, Risks, and Limitations
 
 ### Known Biases
-Potential biases in educational content sources, including cultural or linguistic preferences.
+- The model may reflect cultural or linguistic biases inherent in the training dataset.
 
 ### Risks
-Model may generate incorrect or general responses for ambiguous queries.
+- Outputs may lack precision for ambiguous or highly specialized queries.
+- Inaccurate responses may occur for tasks outside the educational domain.
 
 ### 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.
+- Use this model cautiously in critical applications, ensuring thorough evaluation of outputs for accuracy and bias.
 
-### 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
+- **Primary Metric**: Training loss.
+- **Secondary Metrics**: Accuracy and relevance through manual evaluation.
+
+### Performance Results
+- Achieved low validation loss, indicating strong generalization capabilities for educational tasks.
 
-### Results
-Achieved low validation loss during training, demonstrating generalization capability.
+---
 
 ## Environmental Impact
 
-- **Hardware**: NVIDIA A100
-- **Training Duration**: 26 hours
+- **Hardware Utilized**: NVIDIA A100 GPUs.
+- **Training Time**: 26 hours.
+- **Optimizations**: Quantization and efficient fine-tuning methods to reduce resource usage.
+
+---
 
 ## Citation
 
+If you use this model in your work, please cite it as follows:
+
 ```bibtex
 @misc{169Pi_generic_slm,
   title={169Pi/generic_slm: Fine-Tuned Educational Model},
@@ -146,8 +131,12 @@ Achieved low validation loss during training, demonstrating generalization capab
 }
 ```
 
+---
 
 ## Contact
 
+For inquiries or technical support, please contact:
+
 - **Developer**: 169Pi AI  
-- **Email**: [[email protected]](mailto:[email protected])
+- **Email**: [[email protected]](mailto:[email protected])
+```