Exaone

EXAONE-3.5-Instruct-Demo by LG AI Research. The EXAONE 3.5 Instruct Demo is an interactive platform hosted on Hugging Face Spaces, showcasing the capabilities of LG AI Research's EXAONE 3.5 series of instruction-tuned language models. These models are designed to proficiently handle tasks in both English and Korean, with parameter sizes of 2.4B, 7.8B, and 32B. Here's what you can gather from the available information:

• EXAONE 3.5 Series: This includes instruction-tuned language models in three sizes: 2.4B, 7.8B, and 32B. These models are designed for real-world use cases, focusing on instruction-following capabilities and long-context understanding. They are competitive in performance against similar-sized models in various benchmarks.

• Model Details:

  • 2.4B Model: Optimized for deployment on smaller or resource-constrained devices.

  • 7.8B Model: Matches the size of its predecessor but offers improved performance.

  • 32B Model: Provides frontier-level performance with high capabilities in instruction following and long context processing (up to 32K tokens).

• Usage and Inference: The models are available in GGUF format for inference, allowing for easier deployment on various platforms. They recommend using Transformers v4.43 or later for inference with these models. Example code snippets for conversational inference are provided, demonstrating how to use these models with specific prompts in English and Korean.

• Limitations: The models can occasionally produce inappropriate or biased responses due to the statistical nature of how they've been trained. LG AI Research emphasizes that the model's output does not reflect their views, and users are advised to be aware of potential issues like biased, harmful, or incorrect information.

• Availability and Open-Sourcing: These models have been open-sourced, making them accessible for researchers, developers, and enthusiasts to use, modify, or study.

For more detailed instructions or to interact with the demo:

• Visit the Hugging Face Space directly using the link you provided where you can try out the model interactively.

• For technical details on how to run or integrate these models into your projects, refer to the documentation and code snippets provided on the Hugging Face pages for each model size.

Remember, while these models show strong performance, they are part of ongoing research and development, so there might be updates or newer versions released over time.

If you're interested in the exact performance metrics or more in-depth technical details, you might want to check the technical report, blog posts, or GitHub links mentioned in the model descriptions on Hugging Face.