LocalLLaMA

Hello y'all, i was using this guide to try and set up llama again on my machine, i was sure that i was following the instructions to the letter but when i get to the part where i need to run setup_cuda.py install i get this error

File "C:\Users\Mike\miniconda3\Lib\site-packages\torch\utils\cpp_extension.py", line 2419, in _join_cuda_home raise OSError('CUDA_HOME environment variable is not set. ' OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root. (base) PS C:\Users\Mike\text-generation-webui\repositories\GPTQ-for-LLaMa>

i'm not a huge coder yet so i tried to use setx to set CUDA_HOME to a few different places but each time doing echo %CUDA_HOME doesn't come up with the address so i assume it failed, and i still can't run setup_cuda.py

Anyone have any idea what i'm doing wrong?

You type "Once upon a time!!!!!!!!!!" and those exclamation marks are rendered to show the LLM generated text, using a tiny 30MB model

via https://simonwillison.net/2024/Jun/23/llama-ttf/

Hello! I am looking for some expertise from you. I have a hobby project where Phi-3-vision fits perfectly. However, the PyTorch version is a little too big for my 8GB video card. I tried looking for a quantized model, but all I found is 4-bit. Unfortunately, this model works too poorly for me. So, for the first time, I came across the task of quantizing a model myself. I found some guides for Phi-3V quantization for ONNX. However, the only options are fp32(?), fp16, int4. Then, I found a nice tool for AutoGPTQ but couldn't make it work for the job yet. Does anybody know why there is no int8/int6 quantization for Phi-3-vision? Also, has anybody used AutoGPTQ for quantization of vision models?

"Alice has N brothers and she also has M sisters. How many sisters does Alice’s brother have?"

The problem has a light quiz style and is arguably no challenge for most adult humans and probably to some children.

The scientists posed varying versions of this simple problem to various State-Of-the-Art LLMs that claim strong reasoning capabilities. (GPT-3.5/4/4o , Claude 3 Opus, Gemini, Llama 2/3, Mistral and Mixtral, including very recent Dbrx and Command R+)

They observed a strong collapse of reasoning and inability to answer the simple question as formulated above across most of the tested models, despite claimed strong reasoning capabilities. Notable exceptions are Claude 3 Opus and GPT-4 that occasionally manage to provide correct responses.

This breakdown can be considered to be dramatic not only because it happens on such a seemingly simple problem, but also because models tend to express strong overconfidence in reporting their wrong solutions as correct, while often providing confabulations to additionally explain the provided final answer, mimicking reasoning-like tone but containing nonsensical arguments as backup for the equally nonsensical, wrong final answers.

• Arxiv, paper submitted on 4 Jun 2024
• PDF
• heise online article (German)

Remember 2-3 years ago when OpenAI had a website called transformer that would complete a sentence to write a bunch of text. Most of it was incoherent but I think it is important for historic and humor purposes.

So here's the way I see it; with Data Center profits being the way they are, I don't think Nvidia's going to do us any favors with GPU pricing next generation. And apparently, the new rule is Nvidia cards exist to bring AMD prices up.

So here's my plan. Starting with my current system;

OS: Linux Mint 21.2 x86_64  
CPU: AMD Ryzen 7 5700G with Radeon Graphics (16) @ 4.673GHz  
GPU: NVIDIA GeForce RTX 3060 Lite Hash Rate  
GPU: AMD ATI 0b:00.0 Cezanne  
GPU: NVIDIA GeForce GTX 1080 Ti  
Memory: 4646MiB / 31374MiB

I think I'm better off just buying another 3060 or maybe 4060ti/16. To be nitpicky, I can get 3 3060s for the price of 2 4060tis and get more VRAM plus wider memory bus. The 4060ti is probably better in the long run, it's just so damn expensive for what you're actually getting. The 3060 really is the working man's compute card. It needs to be on an all-time-greats list.

My limitations are that I don't have room for full-length cards (a 1080ti, at 267mm, just barely fits), also I don't want the cursed power connector. Also, I don't really want to buy used because I've lost all faith in humanity and trust in my fellow man, but I realize that's more of a "me" problem.

Plus, I'm sure that used P40s and P100s are a great value as far as VRAM goes, but how long are they going to last? I've been using GPGPU since the early days of LuxRender OpenCL and Daz Studio Iray, so I know that sinking feeling when older CUDA versions get dropped from support and my GPU becomes a paperweight. Maxwell is already deprecated, so Pascal's days are definitely numbered.

On the CPU side, I'm upgrading to whatever they announce for Ryzen 9000 and a ton of RAM. Hopefully they have some models without NPUs, I don't think I'll need them. As far as what I'm running, it's Ollama and Oobabooga, mostly models 32Gb and lower. My goal is to run Mixtral 8x22b but I'll probably have to run it at a lower quant, maybe one of the 40 or 50Gb versions.

My budget: Less than Threadripper level.

Thanks for listening to my insane ramblings. Any thoughts?

It actually isn't half bad depending on the model. It will not be able to help you with side streets but you can ask for the best route from Texas to Alabama or similar. The results may surprise you.

Current situation: I've got a desktop with 16 GB of DDR4 RAM, a 1st gen Ryzen CPU from 2017, and an AMD RX 6800 XT GPU with 16 GB VRAM. I can 7 - 13b models extremely quickly using ollama with ROCm (19+ tokens/sec). I can run Beyonder 4x7b Q6 at around 3 tokens/second.

I want to get to a point where I can run Mixtral 8x7b at Q4 quant at an acceptable token speed (5+/sec). I can run Mixtral Q3 quant at about 2 to 3 tokens per second. Q4 takes an hour to load, and assuming I don't run out of memory, it also runs at about 2 tokens per second.

What's the easiest/cheapest way to get my system to be able to run the higher quants of Mixtral effectively? I know that I need more RAM Another 16 GB should help. Should I upgrade the CPU?

As an aside, I also have an older Nvidia GTX 970 lying around that I might be able to stick in the machine. Not sure if ollama can split across different brand GPUs yet, but I know this capability is in llama.cpp now.

Thanks for any pointers!

Recently OpenAI released GPT-4o

Video I found explaining it: https://youtu.be/gy6qZqHz0EI

Its a little creepy sometimes but the voice inflection is kind of wild. What I the to be alive.

I am planning my first ai-lab setup, and was wondering how many tokens different AI-workflows/agent network eat up on an average day. For instance talking to an AI all day, have devlin running 24/7 or whatever local agent workflow is running.

Oc model inference speed and type of workflow influences most of these networks, so perhaps it's easier to define number of token pr project/result ?

So I were curious about what typical AI-workflow lemmies here run, and how many tokens that roughly implies on average, or on a project level scale ? Atmo I don't even dare to guess.

Thanks..

Hartford is credited as creator of Dolphin-Mistral, Dolphin-Mixtral and lots of other stuff.

He's done a huge amount of work on uncensored models.

From Simon Willison: "Mistral tweet a link to a 281GB magnet BitTorrent of Mixtral 8x22B—their latest openly licensed model release, significantly larger than their previous best open model Mixtral 8x7B. I’ve not seen anyone get this running yet but it’s likely to perform extremely well, given how good the original Mixtral was."

I've been using tie-fighter which hasn't been too bad with lorebooks in tavern.

Afaik most LLMs run purely on the GPU, dont they?

So if I have an Nvidia Titan X with 12GB of RAM, could I plug this into my laptop and offload the load?

I am using Fedora, so getting the NVIDIA drivers would be... fun and already probably a dealbreaker (wouldnt want to run proprietary drivers on my daily system).

I know that using ExpressPort adapters people where able to use GPUs externally, and this is possible with thunderbolt too, isnt it?

The question is, how well does this work?

Or would using a small SOC to host a webserver for the interface and do all the computing on the GPU make more sense?

I am curious about the difficulties here, ARM SOC and proprietary drivers? Laptop over USB-c (maybe not thunderbolt?) and a GPU just for the AI tasks...