169Pi/NeuroBit_1.0

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:

@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]