license: other
inference: false
gpt4-x-vicuna-13B-GGML
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.
THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508
I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit 2d5db48 or later) to use them.
For files compatible with the previous version of llama.cpp, please see branch previous_llama_ggmlv2.
Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
|---|---|---|---|---|---|
gpt4-x-vicuna-13B.ggmlv3.q4_0.bin |
q4_0 | 4bit | 8.14GB | 10GB | 4-bit. |
gpt4-x-vicuna-13B.ggmlv3.q4_1.bin |
q4_1 | 4bit | 8.95GB | 10GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
gpt4-x-vicuna-13B.ggmlv3.q5_0.bin |
q5_0 | 5bit | 8.95GB | 11GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
gpt4-x-vicuna-13B.ggmlv3.q5_1.bin |
q5_1 | 5bit | 9.76GB | 12GB | 5-bit. Even higher accuracy, higher resource usage and slower inference. |
gpt4-x-vicuna-13B.ggmlv3.q8_0.bin |
q8_0 | 8bit | 16GB | 18GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
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.ggmlv3.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.
Want to support my work?
I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.
So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.
Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.
- Patreon: coming soon! (just awaiting approval)
- Ko-Fi: https://ko-fi.com/TheBlokeAI
- Discord: https://discord.gg/UBgz4VXf
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/