Text Generation
PEFT
Safetensors
GGUF
English
materialsanalyst-ai-7b
MaterialsAnalyst-AI-7B
materials-science
computational-materials
materials-analysis
chain-of-thought
reasoning-model
property-prediction
materials-discovery
crystal-structure
materials-informatics
scientific-ai
7b
quantized
fine-tuned
lora
json-mode
structured-output
materials-engineering
band-gap-prediction
computational-chemistry
materials-characterization
Update README.md
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README.md
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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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##
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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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**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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# LLaMA.cpp deployment (CPU optimized)
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python Scripts/Inference_llama.cpp.py
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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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## Input/Output Format
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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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## 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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**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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**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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# LLaMA.cpp deployment (CPU optimized)
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python Scripts/Inference_llama.cpp.py
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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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## Repository Contents
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- **Scripts/** - Inference scripts for SafeTensors and LLaMA.cpp deployment
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