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README.md
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---
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license: mit
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---
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license: mit
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datasets:
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- Taylor658/photonic-integrated-circuit-yield
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language:
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- en
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---
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# Model Card
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## Model Overview 🦙✨
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**Model Name:** Photonics_Distill_Llama_70B
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**Model Type:** Distilled Reasoning Model
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**Languages:** English
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**License:** MIT
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Photonics_Distill_Llama_70B is a distilled reasoning model engineered to excel at advanced logical inference and domain-specific problem solving. It is distilled from a larger reasoning model, then further fine-tuned using reinforcement learning 🚀 on the **photonic_integrated_circuit_yield** dataset. This process refines its performance on complex tasks in photonics and integrated circuit yield optimization, making it a great tool for researchers and professionals.
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## Model Details 🔧
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**Developers:** A Taylor
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**Model Architecture:** Transformer-based model enhanced with distillation techniques to optimize reasoning performance
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**Parameters:** 70 Billion
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**Native Function Calling:** Supported
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**Multimodal Capabilities:** Supports Multimodal Use Cases
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## Intended Use 🎯
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**Primary Applications:**
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- Assist photonics researchers and engineers in analyzing and predicting integrated circuit yield.
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- Provide detailed computational reasoning for design optimization and troubleshooting in photonic manufacturing.
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- Serve as an educational resource by offering clear explanations and insights based on simulation and experimental data.
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**Usage Scenarios:**
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- Explaining how specific variations in photonic design parameters (e.g., waveguide dimensions) impact yield.
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- Interpreting simulation data and theoretical models in photonic research.
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- Offering recommendations for improving manufacturing processes and design strategies in integrated photonics.
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## Training Data 📚
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**Dataset Name:** photonic_integrated_circuit_yield
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**Description:**
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A comprehensive dataset comprising synthetic simulation results, computational models, and theoretical analyses pertinent to photonic integrated circuits yield. This dataset is **entirely generated through synthetic data creation techniques**, designed to simulate a wide range of manufacturing scenarios, yield metrics, and performance benchmarks. It enables the model to learn nuanced reasoning strategies in photonic applications without relying on real-world experimental data.
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**Data Modalities:**
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- **Text:** Artificially generated research articles, technical reports, and simulation summaries.
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- **Code:** Simulation scripts and algorithms relevant to photonic circuit analysis, crafted to mimic real-world processes.
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## Training Procedure ⚙️
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The model is fine-tuned via a reinforcement learning framework. Key enhancements include:
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- **Domain-Specific Fine-Tuning:** Leveraging the synthetic photonic_integrated_circuit_yield dataset to adjust model parameters for optimal performance in simulated photonic reasoning tasks.
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- **Reinforcement Learning:** Utilizing reward-based feedback 🚀 to reinforce accurate, insightful, and contextually relevant responses based on synthetic data.
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- **Validation and Testing:** Rigorous evaluation against established simulation benchmarks and theoretical models to ensure reliable performance.
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- **Iterative Refinement:** Incorporating continuous feedback from domain experts to progressively improve the model’s output quality.
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## How to Use 💡
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**Input Format:**
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The model accepts natural language queries or prompts focused on photonic integrated circuits, yield analysis, simulation data interpretation, and related technical topics.
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**Examples:**
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- "How does a variation in waveguide width affect the overall yield of a photonic integrated circuit according to synthetic simulation models?"
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- "What simulation parameters are most critical when assessing yield in photonic manufacturing processes using synthetic data?"
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- "Explain the influence of material properties on photonic integrated circuit performance based on recent synthetic data."
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## Limitations ⚠️
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- **Work in Progress:** The model is under continuous development; performance improvements and updates are expected over time.
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- **Domain Specificity:** Optimized for photonic integrated circuits yield analysis; performance may degrade when applied to unrelated domains.
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- **Synthetic Data Disclaimer:** As the model is trained exclusively on synthetic data, its outputs should be validated against real-world data and expert judgment when applied to practical scenarios.
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## Ethical Considerations 🤝
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- **Accuracy:** **Intended as a research and educational aid**, the model should complement rather than replace expert judgment, especially in high-stakes applications.
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- **Transparency:** **Users must be aware that the model’s insights are derived from synthetic data** and may not fully capture the complexities of real-world photonic manufacturing.
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## License 📜
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- **Model License:** MIT
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## Future Work 🔮
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- **Enhanced Reasoning Capabilities:** Further refine reinforcement learning strategies to boost the model’s reasoning depth and accuracy.
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- **Expanded Domain Coverage:** Integrate additional synthetic datasets related to photonic design and manufacturing to broaden the model's expertise.
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- **Performance Optimization:** Explore methods to reduce computational overhead without compromising performance and accuracy.
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## Contact Information 📧
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**Author:** https://huggingface.co/Taylor658
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