license: other
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
- 4bit GPTQ models for GPU inference
- 4bit and 5bit GGML models for CPU inference
- Eric's unquantised model in HF format
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.
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.
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.
