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WizardLM: An Instruction-following LLM Using Evol-Instruct

These files are the result of merging the delta weights with the original Llama7B model.

The code for merging is provided in the WizardLM official Github repo.

WizardLM-7B GGML

This repo contains GGML files for for CPU inference using llama.cpp.

Other repositories available


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
WizardLM-7B.GGML.q4_0.bin q4_0 4bit 4.2GB 6GB 4bit.
WizardLM-7B.GGML.q4_1.bin q4_0 4bit 4.63GB 6GB 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
WizardLM-7B.GGML.q5_0.bin q5_0 5bit 4.63GB 7GB 5-bit. Higher accuracy, higher resource usage and slower inference.
WizardLM-7B.GGML.q5_1.bin q5_1 5bit 5.0GB 7GB 5-bit. Even higher accuracy, and higher resource usage and slower inference.
WizardLM-7B.GGML.q8_0.bin q8_0 8bit 8GB 10GB 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 18 -m WizardLM-7B.GGML.q4_0.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 18 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.


For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Original model info

Overview of Evol-Instruct Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.

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