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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- 4-bit |
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- NeuroBit |
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- EducationalLLM |
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- LoRA |
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- PEFT |
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- Quantization |
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--- |
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# NeuroBit-1.0-Exp |
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## Overview |
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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. |
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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. |
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--- |
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## Key Features |
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- **Base Model**: Meta-Llama-3.1-8B, optimized with LoRA, PEFT, and RSLoRA techniques. |
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- **Parameter Efficiency**: Quantized to 4-bit for improved performance. |
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- **Target Audience**: Students, educators, and developers of educational technology. |
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- **Applications**: Summarization, curriculum-aligned Q&A, practice question generation, and more. |
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--- |
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## Use Cases |
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### Direct Applications |
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- **Concept Summarization**: Generate concise and accurate summaries of academic material. |
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- **Curriculum-Aligned Q&A**: Deliver precise answers to subject-specific questions. |
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- **Practice Material Creation**: Develop quizzes, questions, and explanations. |
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- **Study Resource Recommendations**: Suggest tailored learning resources. |
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### Downstream Applications |
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- **Interactive Learning Platforms**: Enhance user engagement with dynamic educational content. |
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- **Educational Chatbots**: Provide on-demand academic assistance. |
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- **Personalized Study Guides**: Create customized study materials for individual learners. |
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- **Automated Assessment Tools**: Generate and evaluate educational content programmatically. |
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### Out-of-Scope Applications |
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- **Legal or Financial Decision-Making**: This model is not suited for applications outside educational contexts. |
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- **Non-Educational Content Generation**: Avoid using the model for tasks unrelated to education. |
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- **High-Precision Non-Educational Use Cases**: The model may not deliver the required precision outside its intended domain. |
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--- |
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## Training Details |
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### Dataset |
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- **Source**: Proprietary educational dataset curated by 169Pi. |
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- **Preprocessing Steps**: |
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- Deduplication of redundant data. |
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- Removal of noisy and irrelevant information. |
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- Text normalization for enhanced consistency. |
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### Model Configuration |
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- **Parameter Size**: 4.65 billion parameters (quantized to 4-bit). |
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- **Hardware Utilized**: NVIDIA A100 GPUs. |
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- **Training Duration**: 26 hours. |
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### Hyperparameters |
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- **Learning Rate**: `5e-5` |
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- **Scheduler**: Cosine |
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- **Batch Size**: 32 per device |
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- **Gradient Accumulation Steps**: 4 |
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- **Epochs**: 3 |
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- **Mixed Precision**: FP16 and BF16 |
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- **Optimizer**: AdamW (8-bit) |
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- **Weight Decay**: 0.05 |
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- **Warmup Steps**: 1000 |
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- **Logging Frequency**: Every 1000 steps |
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- **Evaluation Strategy**: Per 1000 steps |
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- **Model Checkpoints**: Saved every 1000 steps |
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--- |
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## Technical Specifications |
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- **Base Model**: Meta-Llama-3.1-8B |
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- **Quantization**: 4-bit quantization for computational efficiency. |
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- **Fine-Tuning Techniques**: |
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- **LoRA**: Low-Rank Adaptation for Parameter-Efficient Fine-Tuning(PEFT). |
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- **PEFT**: Parameter-Efficient Fine-Tuning. |
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- **RSLoRA**: Residual Scaling with LoRA for enhanced generalization. |
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### Model Objective |
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To generate high-quality educational content tailored for diverse academic needs, including: |
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- Topic Summarization |
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- Question-Answer Generation |
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- Personalized Study Material Creation |
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--- |
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## Biases, Risks, and Limitations |
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### Known Biases |
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- The model may reflect cultural or linguistic biases inherent in the training dataset. |
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### Risks |
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- Outputs may lack precision for ambiguous or highly specialized queries. |
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- Inaccurate responses may occur for tasks outside the educational domain. |
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### Recommendations |
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- Use this model cautiously in critical applications, ensuring thorough evaluation of outputs for accuracy and bias. |
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## Evaluation |
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### Metrics |
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- **Primary Metric**: Training loss. |
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- **Secondary Metrics**: Accuracy and relevance through manual evaluation. |
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### Performance Results |
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- Achieved low validation loss, indicating strong generalization capabilities for educational tasks. |
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## Environmental Impact |
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- **Hardware Utilized**: NVIDIA A100 GPUs. |
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- **Training Time**: 26 hours. |
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- **Optimizations**: Quantization and efficient fine-tuning methods to reduce resource usage. |
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--- |
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## Citation |
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If you use this model in your work, please cite it as follows: |
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```bibtex |
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@misc{169Pi_neuroBit-1.0-exp, |
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title={169Pi/neuroBit-1.0-exp: Fine-Tuned Educational Model}, |
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author={169Pi}, |
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year={2024}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/169Pi/neurobit_1.0} |
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} |
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``` |
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--- |
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## Blog |
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https://169pi.ai/research/efficient-4-bit-ai-models%3A-quantization-and-lora-for-scalable-educational-deployment |
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## Contact |
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For inquiries or technical support, please contact: |
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- **Developer**: 169Pi AI |
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- **Email**: [[email protected]](mailto:[email protected]) |
