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@@ -17,29 +17,7 @@ A specialized **open source** AI model designed to assist materials scientists a
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  - **Performance Benchmarking**: Compares materials against industry standards
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  - **Structured Reasoning**: Provides both detailed analysis and concise conclusions
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- ## Quick Start
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-
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- **Install dependencies:**
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- ```bash
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- pip install torch transformers accelerate safetensors
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- # For LLaMA.cpp option: pip install llama-cpp-python
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- ```
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-
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- **Run analysis:**
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- ```bash
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- # SafeTensors deployment (recommended)
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- python Scripts/Inference_safetensors.py
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-
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- # LLaMA.cpp deployment (CPU optimized)
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- python Scripts/Inference_llama.cpp.py
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- ```
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-
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- **Customize your analysis:**
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- - Edit the `JSON_INPUT` variable in either script with your materials data
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- - Modify the `model_path` variable to point to your model files
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- - Common data sources: Materials Project, AFLOW, DFT calculations, experimental databases
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-
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- ## Input/Output Format
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  ### Input Data
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  Provide materials data as JSON with properties, structure, and characteristics:
@@ -109,6 +87,28 @@ Thermal behavior: 490 W/m路K conductivity ideal for heat dissipation application
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  Superior combination of thermal, mechanical, and electronic properties makes SiC ideal for demanding high-temperature and high-power applications.
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  ```
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  ## Repository Contents
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  - **Scripts/** - Inference scripts for SafeTensors and LLaMA.cpp deployment
 
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  - **Performance Benchmarking**: Compares materials against industry standards
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  - **Structured Reasoning**: Provides both detailed analysis and concise conclusions
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+ ## Example Analysis
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Input Data
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  Provide materials data as JSON with properties, structure, and characteristics:
 
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  Superior combination of thermal, mechanical, and electronic properties makes SiC ideal for demanding high-temperature and high-power applications.
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  ```
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+ ## Quick Start
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+
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+ **Install dependencies:**
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+ ```bash
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+ pip install torch transformers accelerate safetensors
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+ # For LLaMA.cpp option: pip install llama-cpp-python
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+ ```
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+
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+ **Run analysis:**
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+ ```bash
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+ # SafeTensors deployment (recommended)
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+ python Scripts/Inference_safetensors.py
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+
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+ # LLaMA.cpp deployment (CPU optimized)
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+ python Scripts/Inference_llama.cpp.py
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+ ```
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+
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+ **Customize your analysis:**
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+ - Edit the `JSON_INPUT` variable in either script with your materials data
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+ - Modify the `model_path` variable to point to your model files
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+ - Common data sources: Materials Project, AFLOW, DFT calculations, experimental databases
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+
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  ## Repository Contents
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  - **Scripts/** - Inference scripts for SafeTensors and LLaMA.cpp deployment