AutoGPTQ
koboldcpp
AutoGPTQ | koboldcpp | |
---|---|---|
19 | 180 | |
3,806 | 3,817 | |
5.0% | - | |
9.3 | 10.0 | |
4 days ago | 5 days ago | |
Python | C++ | |
MIT License | GNU Affero General Public License v3.0 |
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AutoGPTQ
- Setting up LLAMA2 70B Chat locally
- Experience of setting up LLAMA 2 70B Chat locally
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GPT-4 Details Leaked
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...
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Loader Types
AutoGPTQ: an attempt at standardizing GPTQ-for-LLaMa and turning it into a library that is easier to install and use, and that supports more models. https://github.com/PanQiWei/AutoGPTQ
- WizardLM-33B-V1.0-Uncensored
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Any help converting an interesting .bin model to 4 bit 128g GPTQ? Bloke?
Just use the script: https://github.com/PanQiWei/AutoGPTQ/blob/main/examples/quantization/quant_with_alpaca.py
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LLM.int8(): 8-Bit Matrix Multiplication for Transformers at Scale
In the wild, people tend to use GTPQ quantization for pure GPU inference: https://github.com/PanQiWei/AutoGPTQ
And ggml's quant for CPU inference with some offload, which just got updated to a more GPTQ-like method days ago: https://github.com/ggerganov/llama.cpp/pull/1684
Some other runtimes like Apache TVM also have their own quant implementations: https://github.com/mlc-ai/mlc-llm
For training, 4-bit bitsandbytes is SOTA, as far as I know.
TBH I'm not sure why this November paper is being linked. Few are running 8 bit models when they could fit a better 3-5 bit model in the same memory pool.
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Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
Instead of integrating GPTQ-for-Lllama, use AutoGPTQ instead.
- AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm
koboldcpp
- Any Online Communities on Local/Home AI?
- Koboldcpp-1.62.1 adds support for Command-R+
- Show HN: I made an app to use local AI as daily driver
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Easiest way to show my model to my mom?
FYI this is the easiest way to host on the horde: https://github.com/LostRuins/koboldcpp
- IT Veteran... why am I struggling with all of this?
- What do you use to run your models?
- ByteDance AI researcher suggests that open source model more powerful than Gemini to be released soon
- i need some help guys
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[Guide] How install KoboldAI in Android via Termux (Update 04-12-2023)
For more information of Koboldcpp look this guide: https://github.com/LostRuins/koboldcpp/wiki
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SillyTavern 1.10.10 has been released
Out of curiosity, is there a specific reason for this? The most popular fork KoboldCpp is in active development, and was the first to adopt the Min P sampler, and even distincts itself with the context shift feature. Just wondering what this means for the future. Thanks!
What are some alternatives?
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
KoboldAI
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
KoboldAI - KoboldAI is generative AI software optimized for fictional use, but capable of much more!
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
ChatRWKV - ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
self-refine - LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
SillyTavern - LLM Frontend for Power Users. [Moved to: https://github.com/SillyTavern/SillyTavern]