Welcome to the intriguing world of local language models! As advancements in AI continue to evolve, the necessity to compare these models to find out which ones shine brightest on consumer hardware becomes essential. In this guide, we’ll explore how to assess various models based on their functionalities and characteristics, providing you both insights and hands-on experiences.

Getting Started with Local LLM Comparison

The original intention of this guide was to analyze smaller models (7B and 13B) that can be efficiently run on consumer-grade hardware. The evaluation will be achieved through scores provided by GPT-4 for a series of questions. However, the influx of feature-rich models necessitated a shift towards allowing users to experiment with the newer models hands-on.

Newer Models without Scores

The following models are available for use directly through Colab. Click the “Open in Colab” button next to each model to get started:

• Zephyr-7B-beta-GGUF
  Hugging Face Link |
  Open in Colab |
  Date Added: 2023-11-27, Roleplay okay, not censored.
• OpenHermes-2.5-Mistral-7B-GGUF
  Hugging Face Link |
  Open in Colab |
  Date Added: 2023-11-27, Roleplay good, not censored.
• Dolphin-2.2.1-Mistral-7B-GGUF
  Hugging Face Link |
  Open in Colab |
  Date Added: 2023-11-27, Roleplay okay, not censored.

Understanding Language Models: An Analogy

Imagine you are at a buffet, and each model is a different dish being served. Just like a buffet, every model has unique flavors and textures (features and functionalities) that cater to different tastes (user needs). Choosing the right dish requires tasting a little of each; similarly, experimenting with different models allows you to discover which fits your specific use case best.

Troubleshooting Tips

While exploring these models, you may encounter some challenges. Here are some troubleshooting ideas to help navigate common issues:

• Ensure you have a stable internet connection as running models via Colab requires it.
• Check for prerequisites like adhering to specific versions of libraries (such as PyTorch or TensorFlow) if you are running locally.
• If you face any errors, revisit the codes or check the report section of the Colab page for potential fixes.
• Need further assistance? Reach out or join discussions with the AI community.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.