9 mo. ago • 95%

Smaug-72B-v0.1: The New Open-Source LLM Roaring to the Top of the Leaderboard

huggingface.co abacusai/Smaug-72B-v0.1 · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We recently released Smaug-72B-v0.1 which has taken first place on the Open LLM Leaderboard by HuggingFace. It is the first open-source model to have an average score more than 80.

60 comments

I'm afraid to even ask for the minimum specs on this thing, open source models have gotten so big lately

45
- Every billion parameters needs about 2 GB of VRAM - if using bfloat16 representation. 16 bits per parameter, 8 bits per byte -> 2 bytes per parameter.
  
  1 billion parameters ~ 2 Billion bytes ~ 2 GB.
  
  From the name, this model has 72 Billion parameters, so ~144 GB of VRAM
  
  49
  
  Ok but will this run on my TI-83? It's a + model.
  
  47
  
  It's been discovered that you can reduce the bits per parameter down to 4 or 5 and still get good results. Just saw a paper this morning describing a technique to get down to 2.5 bits per parameter, even, and apparently it 's fine. We'll see if that works out in practice I guess
  
  11
  
  Though with quantisation you can get it down to like 30GB of vram or less.
  
  1
  
  Any idea what 8Q requirements would be? Or 4 or 5?
  
  1
  
  Llama 2 70B with 8b quantization takes around 80GB VRAM if I remember correctly. I’ve tested it a while ago.
  
  1
- CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.) To run Qwen-72B-Chat in bf16/fp16, at least 144GB GPU memory is required (e.g., 2xA100-80G or 5xV100-32G). To run it in int4, at least 48GB GPU memory is requred (e.g., 1xA100-80G or 2xV100-32G).
  
  It's derived from Qwen-72B, so same specs. Q2 clocks it in at only ~30GB.
  
  18
- Just a data center or two. Easy peasy dirt cheapy.
  
  11
- I think I read somewhere that you'll basically need 130 GB of RAM to load this model. You could probably get some used server hardware for less than $600 to run this.
  
  5
  
  Oh if only it were so simple lmao, you need ~130GB of VRAM, aka the graphics card RAM. So you would need about 9 consumer grade 16GB graphics cards and you'll probably need Nvidia because of fucking CUDA so we're talking about thousands of dollars. Probably approaching 10k
  
  Ofc you can get cards with more VRAM per card, but not in the consumer segment so even more $$$$$$
  
  16
  
  Unless you’re getting used datacenter grade hardware for next to free, I doubt this. You need 130 gb of VRAM on your GPUs
  
  10
- Around 48gb of VRAM if you want to run it in 4bits
  
  3
Oh yay another model I can't run on my computer :'(

13
i thought this was about the MUD server software, and got excited. alas.

13
That's nice and all, but what are some FOSS models I can run on GPU with only 4GB?

I've tried Deepseek Coder, and it's pretty nice for what I use it for. Then there's TinyLlama, which... well it's fast, but I need to be veeeery exact in how I prompt it.

11
- Unfortunately LLMs need a lot of VRAM. You could try using koboldcpp, it runs on the CPU but let's you offload layers onto the GPU. That way you might be able to stay withing those 4gb even with larger models.
  
  Edit: I forgot to mention there's a fork of koboldcpp with rocm for AMD cards, which is about twice as fast if I remember correctly. Only relevant if you have an AMD card tho.
  
  Edit 2: This is the model I use btw
  
  8
  
  I'm currently playing around with the Jan client, which uses the nitro engine. I think I need to read up on it more, because when I set the ngl value to 15 in order to offload 50% to GPU like the Jan guide says, nothing happens. Though that could be an issue specific to Jan.
  
  2
- 4GB is practically nothing in this space. Ideally you want at least 10GB of dedicated vram if you can't get even more. Keep in mind you're also probably trying to share that vram with your operating system. So it's more like ~3GB before you even started.
  
  Kolboldcpp is capable of using both your GPU and CPU together, you might wanna consider that. (Using a feature called layers) There's a trade-off that occurs between the memory available and the quality of its output and the speed of the calculation.
  
  The model mentioned in this post can be run on the CPU with enough system ram or swap.
  
  If you wanna keep it all on the GPU check out 4bit models. Also there's been a lot of work into trying to do this with the raspberry Pi. I suspect that their work could help you out here as well.
  
  7
- Depends on your needs. Best look around in !localllama@sh.itjust.works or similar. (I don't wanna say reddit but r/localLlama is much larger.)
  
  If you're more into creative writing, maybe look for places that discuss SillyTavern (r/SillyTavernAI is an option). It's software for role-play chats, which may not be what you want. But the community is (relatively) large and likely to have good tips for non-coding/less technical applications.
  
  2
Where can we all see the leader board?
Edit: never mind

9
If only I had the $ to get a rig that could run this locally

6
- Since I had an okay experience with EasyDiffusion I tried running text gen locally through oobabooga, but no matter which model I load, it just crashes whenever it tries to generate anything, regardless if it runs through the UI's chat or SillyTavern. No error in the terminal either, it just stops and throws me back into the command line.
  
  3
OOTL: What is a LLM and what does it do?

2
- Large Language Model AI. Like ChatGPT.
  
  22
  
  And at 72 billion parameters it's something you can run on a beefy but not special-purpose graphics card.
  
  0
What a catchy name.

0
- It's pronounced "Smaug"
  
  6
  
  Potato potato
  
  4

You've viewed 60 comments.