README.md

---
license: other
datasets:
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
inference: false
---
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# WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct

These files are GGML format model files for [Eric Hartford's 'uncensored' version of WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored).

GGML files are for CPU inference using [llama.cpp](https://github.com/ggerganov/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](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered).

## Other repositories available

* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)
* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML)
* [Eric's unquantised model in HF format](https://huggingface.co/ehartford/WizardLM-7B-Uncensored)

## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
`WizardLM-7B-uncensored.q4_0.bin` | q4_0 | 4bit | 4.2GB | 6GB | 4-bit. |
`WizardLM-7B-uncensored.q4_1.bin` | q4_1 | 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-uncensored.q5_0.bin` | q5_0 | 5bit | 4.63GB | 7GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
`WizardLM-7B-uncensored.q5_1.bin` | q5_1 | 5bit | 5.0GB | 7GB | 5-bit. Even higher accuracy, resource usage and slower inference.|
`WizardLM-7B-uncensored.q8_0.bin` | q8_0 | 5bit | 9.0GB | 11 | 5-bit. Even higher accuracy, resource usage and slower inference.|

## 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.ggmlv3.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 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](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).

Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files.

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## Discord

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

[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)

## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://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: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**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!
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# 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.

![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_overall.png)
![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_running.png)