llama.cpp
GPTQ-for-LLaMa
| llama.cpp | GPTQ-for-LLaMa | |
|---|---|---|
| 1 | 19 | |
| 1 | 129 | |
| - | - | |
| 9.4 | 7.7 | |
| 11 months ago | 12 months ago | |
| C | Python | |
| MIT License | - |
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llama.cpp
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Anyone actually running 30b/65b at reasonably high speed? What's your rig?
It might be worth trying out 2/3 bit quantization on llama.cpp. Currently sitting in an unmerged pr, but it works. I doubt you’ll be getting 5+ tokens/second though. link
GPTQ-for-LLaMa
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I have tried various different methods to install, and none work. Can you spoon-feed me how?
git clone https://github.com/oobabooga/GPTQ-for-LLaMa
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Query output random text
If you're using the model directly from ehartford, that one hasn't been quantized. Try using the GPTQ quantized version here, and use this fork of GPTQ-for-LLaMa. Load in 4-bit with --wbits 4
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Help needed with installing quant_cuda for the WebUI
This worked for me on Ubuntu. If you want to use the CUDA branch instead of triton, do the same steps except clone this GPTQ-for-LLaMa fork and run python setup_cuda.py install
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AutoGPTQ vs GPTQ-for-llama?
If you don't have triton and you use AutoGPTQ you're gonna notice a huge slow down compared to the old GPTQ-for-LLaMA cuda branch. For me AutoGPTQ gives me a whopping 1 token per second compared to the old GPTQ that gives me a decent 9 tokens per second.. both times I used a same sized model. (I think the slowdown is due to AutoGPTQ using the newer cuda branch which is much slower than the old one)
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Guanaco 7B, 13B, 33B and 65B models by Tim Dettmers: now for your local LLM pleasure
Are you using a later version of GPTQ-for-LLaMa? If so, go to ooba's CUDA fork (https://github.com/oobabooga/GPTQ-for-LLaMa). That's what I made it in and it definitely works with that. And that's what's included in the one-click-installers.
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Any idea Vicuna 13B 4bit model output random content?
This usually happens when using models that conflict with your GPTQ installation. You should be using this fork: https://github.com/oobabooga/GPTQ-for-LLaMa. If you did the manual installation wrong, use the one click installer instead.
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GPT4All: A little helper to get started
cd text-generation-webui # wherever you have it installed mkdir -p repositories cd repositories git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda GPTQ-for-LLaMa cd GPTQ-for-LLaMa python setup_cuda install
- wizard-vicuna-13B • Hugging Face
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Anyone actually running 30b/65b at reasonably high speed? What's your rig?
I'm on GPTQ for LLaMA folder under repositories says it's pointed at https://github.com/oobabooga/GPTQ-for-LLaMa.git. But I've run through the instructions and also applied the monkey patch to train and apply 4 bit lora which may come into play. No idea.
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Trying to run TheBloke/vicuna-13B-1.1-GPTQ-4bit-128g with latest GPTQ-for-LLaMa CUDA branch
git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda
What are some alternatives?
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
one-click-installers - Simplified installers for oobabooga/text-generation-webui.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
SillyTavern - LLM Frontend for Power Users.
Local-LLM-Comparison-Colab-UI - Compare the performance of different LLM that can be deployed locally on consumer hardware. Run yourself with Colab WebUI.
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
serge - A web interface for chatting with Alpaca through llama.cpp. Fully dockerized, with an easy to use API.
starcoder - Home of StarCoder: fine-tuning & inference!