license: gpl
inference: false
gpt4-x-vicuna-13B-GPTQ
These files are GGML format model files of NousResearch's gpt4-x-vicuna-13b.
GGML files are for CPU inference using llama.cpp.
Repositories available
- 4bit GPTQ models for GPU inference.
- 4bit and 5bit GGML models for CPU inference.
- float16 HF model for unquantised and 8bit GPU inference.
Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
|---|---|---|---|---|---|
gpt4-x-vicuna-13B.ggml.q4_0.bin |
q4_0 | 4bit | 8.14GB | 10GB | Maximum compatibility |
gpt4-x-vicuna-13B.ggml.q4_2.bin |
q4_2 | 4bit | 8.14GB | 10GB | Best compromise between resources, speed and quality |
gpt4-x-vicuna-13B.ggml.q5_0.bin |
q5_0 | 5bit | 8.95GB | 11GB | Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources. |
gpt4-x-vicuna-13B.ggml.q5_1.bin |
q5_1 | 5bit | 9.76GB | 12GB | Brand new 5bit method. Slightly higher resource usage than q5_0. |
- The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
- The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
- The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
- The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1.
q4_2 compatibility
q4_2 is a relatively new 4bit quantisation method offering improved quality. However they are still under development and their formats are subject to change.
In order to use these files you will need to use recent llama.cpp code. And it's possible that future updates to llama.cpp could require that these files are re-generated.
If and when the q4_2 file no longer works with recent versions of llama.cpp I will endeavour to update it.
If you want to ensure guaranteed compatibility with a wide range of llama.cpp versions, use the q4_0 file.
q5_0 and q5_1 compatibility
These new methods were released to llama.cpp on 26th April. You will need to pull the latest llama.cpp code and rebuild to be able to use them.
Don't expect any third-party UIs/tools to support them yet.
How to run in llama.cpp
I use the following command line; adjust for your tastes and needs:
./main -t 12 -m gpt4-x-vicuna-13B.ggml.q4_2.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a story about llamas
### Response:"
Change -t 12 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
Note: at this time text-generation-webui will not support the new q5 quantisation methods.
Thireus has written a great guide on how to update it to the latest llama.cpp code so that these files can be used in the UI.
Original model card
As a base model used https://huggingface.co/eachadea/vicuna-13b-1.1
Finetuned on Teknium's GPTeacher dataset, unreleased Roleplay v2 dataset, GPT-4-LLM dataset, and Nous Research Instruct Dataset
Approx 180k instructions, all from GPT-4, all cleaned of any OpenAI censorship/"As an AI Language Model" etc.
Base model still has OpenAI censorship. Soon, a new version will be released with cleaned vicuna from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltere
Trained on 8 A100-80GB GPUs for 5 epochs following Alpaca deepspeed training code.
Nous Research Instruct Dataset will be released soon.
GPTeacher, Roleplay v2 by https://huggingface.co/teknium
Wizard LM by https://github.com/nlpxucan
Nous Research Instruct Dataset by https://huggingface.co/karan4d and https://huggingface.co/huemin
Compute provided by our project sponsor https://redmond.ai/