81–90 of 187 posts
llama.cpp is like the ffmepg of AI, and one of the reasons I so greatly dislike ollama is that the latter completely obfuscates that they're a rebrand of the former. Georgi Gerganov and team did all the hard work; ollama is langchain-like VC-bait with a HF download wrapper.
llama.cpp is like the ffmepg of AI, and one of the reasons I so greatly dislike ollama is that the latter completely obfuscates that they're a rebrand of the former. Georgi Gerganov and team did all the hard work; ollama is langchain-like VC-bait with a HF download wrapper.
Anything that suggests curl into bash just plain sketches me out. (edit: I know, this isn't totally rational, it just seems weird to me. We download and trust a lot of software and run code from a bunch of package repositories as a regular activity...). Git clone llama.cpp and build it, it's not hard. https://github.com/ggml-org/llama.cpp/blob/master/docs/build... literally just a few steps for the basics: git clone…
You're not wrong about the appearance of sketchiness of that, but.... dude, come on. "It's not hard" is only true because you already know how to do it.
llama.cpp is like the ffmepg of AI, and one of the reasons I so greatly dislike ollama is that the latter completely obfuscates that they're a rebrand of the former. Georgi Gerganov and team did all the hard work; ollama is langchain-like VC-bait with a HF download wrapper.
llama.cpp is like the ffmepg of AI, and one of the reasons I so greatly dislike ollama is that the latter completely obfuscates that they're a rebrand of the former. Georgi Gerganov and team did all the hard work; ollama is langchain-like VC-bait with a HF download wrapper.
llama.cpp will happily download models from hugging face, btw.
and? whats the point of this? Doesn't everyone already know about llama.cpp?
Yesterday I installed llama.cpp to test it with local AI Data Analyst that I'm building. I was also testing other open LLM providers: Ollama, Jan, vLLM, LM Studio. I had older NVIDIA card (RTX 3070) and llama.cpp instalation was smooth, contrary to vLLM which required me to reinstall CUDA drivers because by default it installed the latest one. I'm curious if there is a speed difference between the same open LLM model…
> I had older NVIDIA card (RTX 3070) and llama.cpp instalation was smooth what model was it that you were able to run with the rtx 3070?
and? whats the point of this? Doesn't everyone already know about llama.cpp?
Not sure why it's on the front page now, but I highly recommend using llama.cpp for running AI model locally vs using other inference framework, unless you have a very specific requirement. ggerganov and the team have done a stellar job maintaining the quality while still being fast to implement new models/improvements.
At this point the options are llama-server or vLLM if you're serious about running things at your desk in the under 256GB RAM size class (70B, 120B size models). In addition to, of course, 27B to 35B size things. With of course a ton of compile time build customization options for whatever specific hardware platform you want to run either llama or vllm on.
Which last time I checked, both use different formats of the weights, the former GGUF while the latter .safetensors. I mostly end up using vLLM these days and I'm a bit more performance sensitive than what I used to be. Just a shame it's a hassle to share the weights between them with conversion and what not, either batched or on-startup.
I'm confused, is this from Meta? There's no attribution anywhere. Surely releasing an AI tool called llama breaks their trademark if not
It's from https://github.com/ggml-org/llama.cpp -- not associated with Meta, it's been around for years, and surely they know about it -- so I would guess either it's not a trademark violation or they don't care.
This post (https://news.ycombinator.com/item?id=35100086) from march 2023 says in the title "Llama.cpp: Port of Facebook's LLaMA model in C/C++"