Voyager VS GPTQ-for-LLaMa

Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.

> AI cannot sustain itself trained on AI work.

This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]

> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.

You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].

> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.

Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.

I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.

[0]: https://arxiv.org/abs/2309.05463

[1]: https://voyager.minedojo.org/

In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.

I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.

But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.

And yes I think the idea is that emergent behaviour can be useful. This comes to mind

https://github.com/MineDojo/Voyager

But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.

This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.

In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376

Does this beat the voyager? I read about it and wondered what if we add a skill library to langchain/llamaindex agents. It could be the same vector store for storing static data but after each task is performed, the agent will evaluate and archive the recipe of steps to perform a new task. Next time when the agent is asked to perform a task, it can just look at the library to retrieve a recipe. Unlike traditional fine tuning, you dont update the model parameters, these recipes are much more interpretable and can be manually edited/inserted by humans. There may also be an automatic way to convert wikihow articles or youtube tutorials into recipes.

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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