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Instella
Overview :
Instella is a series of high-performance open-source language models developed by the AMD GenAI team, trained on AMD Instinct? MI300X GPUs. This model significantly outperforms other open-source language models of the same size and is comparable in functionality to models like Llama-3.2-3B and Qwen2.5-3B. Instella provides model weights, training code, and training data, aiming to promote the development of open-source language models. Its main advantages include high performance, open-source availability, and optimized support for AMD hardware.
Target Users :
This product is suitable for researchers, developers, and enterprise users who need a high-performance language model, especially those who need open-source solutions to reduce costs and maintain flexibility. AMD hardware optimization makes it particularly suitable for users running within the AMD ecosystem.
Use Cases
Researchers can use the Instella model for academic research to explore new methods in natural language processing.
Enterprises can leverage Instella's high-performance language generation capabilities to develop intelligent customer service systems or content generation tools.
Developers can customize Instella based on the open-source model weights and code to meet specific business needs.
Features
Provides high-performance language generation capabilities, suitable for various natural language processing tasks.
Open-sources model weights and training code, facilitating customization and extension by developers.
Supports AMD Instinct? MI300X GPUs, optimizing hardware performance.
Provides pre-trained and instruction-tuned models to meet the needs of different application scenarios.
Supports multi-node distributed training to accelerate the model training process.
How to Use
1. Install PyTorch and related dependencies, ensuring the environment supports AMD GPUs.
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2. Clone the Instella repository and install dependencies such as Flash-Attention.
3. Load pre-trained models using the Hugging Face interface.
4. Perform model inference or fine-tuning as needed.
5. Use training scripts for multi-node distributed training (optional).
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