metadata

license: other
inference: true
tags:
  - vicuna

Q. Why quantized in 8bit instead of 4bit? A. In theory, a 8bit quantized model should provide slightly better perplexity (maybe not noticeable - To Be Evaluated...) over a 4bit quatized version. If your available GPU VRAM is over 15GB you may want to try this out. Note that quatization in 8bit does not mean loading the model in 8bit precision. Loading your model in 8bit precision (--load-in-8bit) definitely comes with a non-linear quality (perplexity) degradation.

Refs:

This model is a 8bit quantization of Vicuna 13B.

13B parameters
Group size: 128
wbits: 8
true-sequential: yes
act-order: yes
8-bit quantized - Read more about this here: https://github.com/ggerganov/llama.cpp/pull/951
Conversion process: LLaMa 13B -> LLaMa 13B HF -> Vicuna13B-v1.1 HF -> Vicuna13B-v1.1-8bit-128g

Basic installation procedure

It was a nightmare, I will only detail briefly what you'll need. WSL was quite painful to sort out.
I will not provide installation support, sorry.
You can certainly use llama.cpp and other loaders that support 8bit quantization, I just chose oobabooga/text-generation-webui.
You will likely face many bugs until text-generation-webui loads, ranging between missing PATH or env variables to having to manually pip uninstall/install packages.
The notes below will likely become outdated once both text-generation-webui and GPTQ-for-LLaMa receive the appropriate bug fixes.
If this model produces very slow answers (1 token/s), it means you are not using Cuda for bitsandbytes or that your hardware needs an upgrade.
If this model produces answers with weird characters, it means you are not using the correct version of qwopqwop200/GPTQ-for-LLaMa as mentioned below.
If this model produces answers that are out of topic or if it talks to itself, it means you are not using the correct checkout 508de42 of qwopqwop200/GPTQ-for-LLaMa as mentioned below.

Cuda (Slow tokens/s):

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt

mkdir repositories
cd repositories
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda # Make sure you obtain the qwopqwop200 version, not the oobabooga one! (because "act-order: yes")
cd GPTQ-for-LLaMa
pip install -r requirements.txt
python setup_cuda.py install

Triton (Fast tokens/s) - Works on Windows with WSL (what I've used) or Linux:

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
git fetch origin pull/1229/head:triton # This is the version that supports Triton - https://github.com/oobabooga/text-generation-webui/pull/1229
git checkout triton
pip install -r requirements.txt

mkdir repositories
cd repositories
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git # -b cuda
cd GPTQ-for-LLaMa
git checkout 508de42 # Before qwopqwop200 broke everything... - https://github.com/qwopqwop200/GPTQ-for-LLaMa/issues/183
pip install -r requirements.txt

Testbench detail and results

Latest version of oobabooga + https://github.com/oobabooga/text-generation-webui/pull/1229
NVIDIA GTX 3090
32BG DDR4
i9-7980XE OC @4.6Ghz
11 tokens/s on average with Triton
Preliminary observations: better results than --load-in-8bits (To Be Confirmed)
Tested and working in both chat mode and text generation mode

License

Research only - non-commercial research purposes - other restrictions apply. See inherited LICENSE file from LLaMa.

LLaMA-13B converted to work with Transformers/HuggingFace is under a special license, please see the LICENSE file for details.

https://www.reddit.com/r/LocalLLaMA/comments/12kl68j/comment/jg31ufe/

Vicuna Model Card

Model details

Model type: Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. It is an auto-regressive language model, based on the transformer architecture.

Model date: Vicuna was trained between March 2023 and April 2023.

Organizations developing the model: The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.

Paper or resources for more information: https://vicuna.lmsys.org/

License: Apache License 2.0

Where to send questions or comments about the model: https://github.com/lm-sys/FastChat/issues

Intended use

Primary intended uses: The primary use of Vicuna is research on large language models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

Training dataset

70K conversations collected from ShareGPT.com.

Evaluation dataset

A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.

Major updates of weights v1.1

Refactor the tokenization and separator. In Vicuna v1.1, the separator has been changed from "###" to the EOS token "</s>". This change makes it easier to determine the generation stop criteria and enables better compatibility with other libraries.
Fix the supervised fine-tuning loss computation for better model quality.