metadata

license: apache-2.0
datasets:
  - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
inference: false

WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct

These files are GGML format model files for Eric Hartford's 'uncensored' version of WizardLM.

GGML files are for CPU inference using llama.cpp.

Eric did a fresh 7B training using the WizardLM method, on a dataset edited to remove all the "I'm sorry.." type ChatGPT responses.

Other repositories available

Provided files

Name	Quant method	Bits	Size	RAM required	Use case
`WizardLM-7B-uncensored.q4_0.bin`	q4_0	4bit	4.2GB	6GB	Maximum compatibility
`WizardLM-7B-uncensored.q4_2.bin`	q4_2	4bit	4.2GB	6GB	Best compromise between resources, speed and quality
`WizardLM-7B-uncensored.q5_0.bin`	q5_0	5bit	4.63GB	7GB	Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources.
`WizardLM-7B-uncensored.q5_1.bin`	q5_1	5bit	5.0GB	7GB	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 WizardLM-7B-uncensored.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.

Eric's original model card

This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.

Shout out to the open source AI/ML community, and everyone who helped me out, including Rohan, TheBloke, and Caseus

WizardLM's original model card

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.