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@@ -44,7 +44,7 @@ To run AI locally, you need both an AI model and an AI client.
There are many permissively licensed models available to download. [Hugging Face](https://huggingface.co/models) is a platform that lets you browse, research, and download models in common formats like [GGUF](https://huggingface.co/docs/hub/en/gguf). Companies that provide good open-weights models include big names like Mistral, Meta, Microsoft, and Google. However, there are also many community models and [fine-tuned](https://en.wikipedia.org/wiki/Fine-tuning_\(deep_learning\)) models available. As mentioned above, quantized models offer the best balance between model quality and performance for those using consumer-grade hardware.
To help you choose a model that fits your needs, you can look at leaderboards and benchmarks. The most widely-used leaderboard is the community-driven [LM Arena](https://lmarena.ai). Additionally, the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) focuses on the performance of open-weights models on common benchmarks like [MMLU-Pro](https://arxiv.org/abs/2406.01574). There are also specialized benchmarks which measure factors like [emotional intelligence](https://eqbench.com), ["uncensored general intelligence"](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard), and [many others](https://www.nebuly.com/blog/llm-leaderboards).
To help you choose a model that fits your needs, you can look at leaderboards and benchmarks. The most widely-used leaderboard is the community-driven [LM Arena](https://lmarena.ai). Additionally, the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) focuses on the performance of open-weights models on common benchmarks like [MMLU-Pro](https://arxiv.org/abs/2406.01574). There are also specialized benchmarks which measure factors like [emotional intelligence](https://eqbench.com), ["uncensored general intelligence"](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard), and [many others](https://nebuly.com/blog/llm-leaderboards).
## AI Chat Clients
@@ -67,7 +67,7 @@ To help you choose a model that fits your needs, you can look at leaderboards an
In addition to supporting a large range of text models, Kobold.cpp also supports image generators such as [Stable Diffusion](https://stability.ai/stable-image) and automatic speech recognition tools such as [Whisper](https://github.com/ggerganov/whisper.cpp).
[:octicons-home-16: Homepage](https://github.com/LostRuins/koboldcpp){ .md-button .md-button--primary }
[:octicons-repo-16: Repository](https://github.com/LostRuins/koboldcpp#readme){ .md-button .md-button--primary }
[:octicons-info-16:](https://github.com/LostRuins/koboldcpp/wiki){ .card-link title="Documentation" }
[:octicons-code-16:](https://github.com/LostRuins/koboldcpp){ .card-link title="Source Code" }
[:octicons-lock-16:](https://github.com/LostRuins/koboldcpp/blob/2f3597c29abea8b6da28f21e714b6b24a5aca79b/SECURITY.md){ .card-link title="Security Policy" }
@@ -124,14 +124,14 @@ Ollama simplifies the process of setting up a local AI chat by downloading the A
<div class="admonition recommendation" markdown>
![Llamafile Logo](assets/img/ai-chat/llamafile.png){align=right}
![Llamafile Logo](assets/img/ai-chat/llamafile.webp){align=right}
**Llamafile** is a lightweight, single-file executable that allows users to run LLMs locally on their own computers without any setup involved. It is [backed by Mozilla](https://hacks.mozilla.org/2023/11/introducing-llamafile) and available on Linux, macOS, and Windows.
Llamafile also supports LLaVA. However, it doesn't support speech recognition or image generation.
[:octicons-home-16: Homepage](https://github.com/Mozilla-Ocho/llamafile){ .md-button .md-button--primary }
[:octicons-info-16:](https://github.com/Mozilla-Ocho/llamafile#llamafile){ .card-link title="Documentation" }
[:octicons-repo-16: Repository](https://github.com/Mozilla-Ocho/llamafile#readme){ .md-button .md-button--primary }
[:octicons-info-16:](https://github.com/Mozilla-Ocho/llamafile#quickstart){ .card-link title="Documentation" }
[:octicons-code-16:](https://github.com/Mozilla-Ocho/llamafile){ .card-link title="Source Code" }
[:octicons-lock-16:](https://github.com/Mozilla-Ocho/llamafile#security){ .card-link title="Security Policy" }