LlamaFile: Increase AI Speed Up by 2x-4x

🌟 Unlock the power of AI with LlamaFile! In this video, we'll explore how to seamlessly integrate LlamaFile into your application for fast, efficient AI inference across multiple platforms. Whether you're on Windows, MacOS, or Linux, LlamaFile ensures smooth operation and enhances your Large Language Model (LLM) performance, all with a single file. 🚀 Llamafile: Fastest AI Inference on your CPU. LlamaFile: Speed Up AI by 20-500% on Any Device

📋 What You'll Learn:
LlamaFile Overview: Why it's a game-changer for running AI models locally and privately.
Installation Guide: Step-by-step setup on different devices, including Raspberry Pi and AMD processors.
Application Integration: How to integrate LlamaFile into your projects using Python.
Running Pre-Downloaded Models: Utilize models from ollama and LM Studio for optimal performance.

🔧 Key Features:
Cross-platform compatibility
Open-source and community-driven
No cloud dependency
Performance on par with GPUs
Simple, single-file setup

🔗 Links:

🔗 Resources & Commands:
All the commands and code snippets used in this video are available in the description below.
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#AI #Inference #LlamaFile

Timestamps:
0:00 - Introduction to LlamaFile
1:02 - Overview & Features of LlamaFile
2:35 - Installing and Running LlamaFile
4:19 - Integrating LlamaFile in Applications
6:23 - Using Pre-Downloaded Models with LlamaFile
8:33 - Final Thoughts

[Techonsapevole]

cool, which is the difference in tokens/s between llamafile and ollama?

[square_and_compass]

Thank you....Explanation, Directions step by step really helps.

[fabsync]

fantastic video! It will be great to set this up without openai.. all open source..->I noticed that you introduced a few open source methods at the end.. Awesome man!

[nbfkxngjmyb]

Now... "This is amazing!" indeed

[florentromanet]

Thanks Mervin. I thought that the Mozilla llamafile project was also created to make a better use of CPUs that have almost been forgotten since we always refer to GPU... Is that correct?

[AleksaMilic-de]

But where did you integrate it with langchain?

[kdpba]

I tried it on a raspberry pi 4 CM with 8 gigs of ram and I used tinylama that they provided. It was at least 3 times slower than ollama in my case. Probably not optimized for arm.

[mictadlo]

How did he combine llama.cpp GUI with Llamfile?

[kiiikoooPT]

3:04 you talk about quantization, isn't this supposed to run on CPU? why do we have quantization for smaller or bigger GPU if this is to run on CPU Your informations are conflicting here.
Do you mean more or less Ram in your pc? Instead of Vram.

[wavecoders]

Tried everything to get this to work on my M1, no success

[anubisai]

Should put your Praison AI app in the links. Thanks for the video!

[focusedstudent]

please make a viedo about using llm in raspberry pi

[AbhijitKrJha]

Hi Mervin, great video. I tried it sometime back with 8 gb ram but was getting unable to allocate sufficient memory error even for I don't have gpu, so can you tell me the cpu requirements to run these files? Also, can you make some videos on onnx and awq model formats, if possible?

[AliAlias]

Very cool ❤✌️😍
Can run it on Android termux? 🙏

[vertigoz]

Afaik for windows there's limits regarding the file size, above 4gb ( don't know the precise size) it doesn't work, you will have to break it in two, the model and the executable

[siddhubhai]

If you're saying for running on server, then nothing better than VLLM, with 24 times faster and parallel processing.

[Soniboy]

Okay now.... WHATS THE CATCH?
Surely you don't get 10x speed with nothing to sacrifice!

[paulham.]

So what ? What else ? Become tiring...😆

[Soniboy]

Okay, same here as well. Trying model on an AMD 6800U with 32GB ram, I get about 2 token/sec. With normal ollama I get about 8 token/sec.
So it's about 4 times SLOWER than just using Ollama itself. I'm running under a freshly installed non virtual Ubuntu env