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+![Nosana Logo](img/nosana_logo.png)
+## Nosana Model Benchmarking
+
+
+### What is Nosana?
+
+Nosana is a decentralized computing network designed to offer cloud services at a fraction of the cost, leveraging the Solana blockchain for secure, fast, and scalable computing resources. It enables developers to run CI/CD pipelines, perform compute-intensive tasks, and more, all within a decentralized ecosystem.
+
+
+### Purpose of This Project
+
+This project aims to benchmark various AI models using the Nosana network, providing insights into performance, efficiency, and scalability. By leveraging a Docker container environment, we ensure consistency, reproducibility, and ease of deployment across the decentralized nodes of the Nosana network.
+
+
+### Models Benchmarked
+| Model Name | Description |
+|------------|-------------|
+| mistralai/Mistral-7B-Instruct-v0.2 | Mistral: |
+| Qwen/Qwen1.5-72B-Chat | Qwen 1.5: |
+| meta-llama/Llama-2-7b | Llama 2: |
+| databricks/dbrx-instruct | DBRX: |
+| 01-ai/Yi-34B-200K | Yi: |
+| xai-org/grok-1 | Grok: |
+
+
+
+### Docker Container
+
+The Docker container is built on an Ubuntu 22.04 base image with CUDA support, ensuring compatibility with GPU-accelerated tasks for AI model benchmarking. The container includes Python 3, pip, PyTorch, and the Hugging Face Transformers library, which are essential for running the models.# nosana-llm-benchmarking