README.md

---
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
- 4-bit
- NeuroBit
- EducationalLLM
- LoRA
- PEFT
- Quantization
---
# NeuroBit-1.0-Exp 

## Overview

The **Neurobit-1.0-Exp** 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. We're naming it **NeuroBit-1.0-Exp** to signify its status as an experimental prototype, pushing the boundaries of innovation.

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
- **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
- **Source**: Proprietary educational dataset curated by 169Pi.
- **Preprocessing Steps**:
  - Deduplication of redundant data.
  - Removal of noisy and irrelevant information.
  - Text normalization for enhanced consistency.

### 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`
- **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).
  - **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
- The model may reflect cultural or linguistic biases inherent in the training dataset.

### Risks
- Outputs may lack precision for ambiguous or highly specialized queries.
- Inaccurate responses may occur for tasks outside the educational domain.

### Recommendations
- Use this model cautiously in critical applications, ensuring thorough evaluation of outputs for accuracy and bias.

---

## Evaluation

### Metrics
- **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.

---

## Environmental Impact

- **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_neuroBit-1.0-exp,
  title={169Pi/neuroBit-1.0-exp: Fine-Tuned Educational Model},
  author={169Pi},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/169Pi/neurobit_1.0}
}
```

---

## Blog
https://169pi.ai/research/efficient-4-bit-ai-models%3A-quantization-and-lora-for-scalable-educational-deployment

## Contact

For inquiries or technical support, please contact:

- **Developer**: 169Pi AI  
- **Email**: [[email protected]](mailto:[email protected])