exllama VS GPTQ-for-LLaMa

not using exllama seems like the tremendous waste

> We serve Llama on 2 80-GB A100 GPUs, as that is the minumum required to fit Llama in memory (with 16-bit precision)

Well there is your problem.

LLaMA quantized to 4 bits fits in 40GB. And it gets similar throughput split between dual consumer GPUs, which likely means better throughput on a single 40GB A100 (or a cheaper 48GB Pro GPU)

https://github.com/turboderp/exllama#dual-gpu-results

Also, I'm not sure which model was tested, but Llama 70B chat should have better performance than the base model if the prompting syntax is right. That was only reverse engineered from the Meta demo implementation recently.

For those getting started, the easiest one click installer I've used is Nomic.ai's gpt4all: https://gpt4all.io/

This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama.cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J models. It also has API/CLI bindings.

I just saw a slick new tool https://ollama.ai/ that will let you install a llama2-7b with a single `ollama run llama2` command that has a very simple 1-click installer for Apple Silicon Mac (but need to build from source for anything else atm). It looks like it only supports llamas OOTB but it also seems to use llama.cpp (via Go adapter) on the backend - it seemed to be CPU-only on my MBA, but I didn't poke too much and it's brand new, so we'll see.

For anyone on HN, they should probably be looking at https://github.com/ggerganov/llama.cpp and https://github.com/ggerganov/ggml directly. If you have a high-end Nvidia consumer card (3090/4090) I'd highly recommend looking into https://github.com/turboderp/exllama

For those generally confused, the r/LocalLLaMA wiki is a good place to start: https://www.reddit.com/r/LocalLLaMA/wiki/guide/

I've also been porting my own notes into a single location that tracks models, evals, and has guides focused on local models: https://llm-tracker.info/

Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .

If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...

Exllama for example uses buffers on each card that reduce the amount of VRAM available for model and context, see here. https://github.com/turboderp/exllama/issues/121

For inference step, this repo can help you to use ExLlama to perform inference on an evaluation dataset for the best throughput.

In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.

You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.

That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).

I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)

[1] https://github.com/turboderp/exllama#results-so-far

[2] https://github.com/aigoopy/llm-jeopardy

[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...

[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder

That's a 16GB GPU, you should be able to fit 13B at 4bit: https://github.com/turboderp/exllama

First I want to make it clear this is not a self promotion post. I hope many machine learning people come at me with questions or comments about this project. A little background about myself. I did work on the 4 bits quantization of LLaMA using GPTQ. (https://github.com/qwopqwop200/GPTQ-for-LLaMa). I've been studying AI in-depth for many years now.

Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .

If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...

I use gptq-for-llama - from this https://github.com/qwopqwop200/GPTQ-for-LLaMa and Pygmalion 7B.

exllama is an optimized implementation of GPTQ-for-LLaMa, allowing you to run 4-bit quantized language models with GPU at great speeds.

With a single NVIDIA 3090 and the fastest inference branch of GPTQ-for-LLAMA https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/fastest-i..., I get a healthy 10-15 tokens per second on the 30B models. IMO GGML is great (And I totally use it) but it's still not as fast as running the models on GPU for now.

And exactly what Triton version are they comparing against? I just tried the latest version of this, and on my 4090/12900K I get 77 tokens per second for Llama 7B-128g. My own GPTQ CUDA implementation gets 151 tokens/second on the same model, same hardware. That makes it 96% faster, whereas AWQ is only 79% faster. For 30B-128g I'm currently only getting a 110% speedup over Triton compared to their 178%, but it still seems a little disingenuous to compare against their own CUDA implementation only, when they're trying to present the quantization method as being faster for inference.

Thanks for the explanation. I think some repos, like text generation webui used gptq for llama (I don't know if it's this repo or another one), anyway most repo that I saw use external things (like gptq for llama)

cd ../.. git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install -r requirements.txt mkdir -p ../text-generation-webui/repositories ln -s ../../GPTQ-for-LLaMa ../text-generation-webui/repositories/GPTQ-for-LLaMa

cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa pip install -r requirements.txt

# To use the GPTQ models I need to Install GPTQ-for-LLaMa and the monkey patch mkdir repositories cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install ninja pip install -r requirements.txt cd cd text-generation-webui # download random model python download-model.py xxx/yyy # try to start the gui python server.py # It returns this warning but it runs bin /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. warn("The installed version of bitsandbytes was compiled without GPU support. " /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32