Original video in Chinese.
Key Takeaway
- Llamafile is an innovative project for running local LLMs. It only takes a single file to run, with no installation required, greatly lowering the barrier to local deployment.
- By combining llama.cpp (model inference optimization) and Cosmopolitan Libc (cross-platform executables), Llamafile enables LLMs to run from a single file.
- Llamafile supports multimodality (such as the Llava model) and can generate text as well as describe images.
- Llamafile’s no-install feature makes it easy to share and run on all kinds of terminal devices, helping LLMs become more widespread and useful.
- The article emphasizes Llamafile’s convenience and its positive impact on the local LLM ecosystem.
Full Content
Running LLMs locally only takes one file, and no installation is needed.
Today I’m introducing a project called Llamafile, which is the most interesting project I’ve seen recently.
If you want to try Llamafile quickly, it’s very easy—anyone can do it.
Step one: go to GitHub and download the official LLM file they’ve prepared.
Step two: if you’re on Windows like me, add the .exe suffix to the end of the file name to make it an executable. If you’re on macOS, run this line of command in the terminal to give the system permission to run it.
Step three: use the cd command to enter the folder where the LLM file is located, then copy this command and run it.
At this point, the system will automatically open a local page, and then you can interact with the LLM.
The interface currently looks pretty crude, but all the necessary features are there, and once the project team has time, polishing it up will be easy. Let’s do a quick test.
If it’s generating text, the speed is extremely fast—visibly faster than ChatGPT.
The Llava model supports multimodality, so we can upload an image and let it describe what’s in the picture.
That’s basically how to use Llamafile. This should be the easiest project I’ve ever introduced in terms of getting started. It reminds me of the 1990s, when I first got into the internet, and green edition software was especially popular. Since I was always playing around in internet cafés, this kind of portable software didn’t need to touch the registry, which made it especially convenient.
Llamafile follows the same idea.
Right now, if you want to run an LLM locally, you usually have to install some software, like Ollama, which I introduced before. After installing it, you still need to download the LLM file.
So why not integrate the model part and the runtime part together?
The model part is llama.cpp. It can reduce the model parameters, which means the resources needed for inference are lower, and it can run on computers that aren’t that powerful.
The runtime part is Cosmopolitan Libc. It’s an open-source C library that allows C programs written by developers to be high-performance, small in size, and runnable basically anywhere.
By integrating these two parts into one architecture, running an LLM locally only requires one file. That means the barrier to entry for LLMs is lowered a lot.
Since it’s just one file and doesn’t need installation, you can put it on a USB drive or in cloud storage. If you want, you can convert your favorite model into a Llamafile and then share it with colleagues. People in China are already doing this.
In the ModelScope community, someone has made a Llamafile collection, including domestic open-source LLMs like Qwen and Zero-One. Everyone can go download and try them.
Finally, Llamafile also supports multiple systems and multiple CPU architectures, and it also supports GPU execution. You can imagine converting a small model into a Llamafile, and then it can run on all kinds of terminal devices. That makes the popularization and application of LLMs much easier all at once.
After I finish this video, I’m planning to try it myself too. It feels like my AI tool library can be upgraded again.