File size: 3,038 Bytes
22cae5b 59371f8 22cae5b 59371f8 ff1c30e 59371f8 37bde1f 8a694bb 858f870 8a694bb 858f870 8a694bb 858f870 8a694bb 59371f8 8a694bb 9688e0a efc44ea 8a694bb efc44ea 8a694bb 4f9e5dc 8a694bb 4f9e5dc 59371f8 ecd42d9 59371f8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
license: other
inference: false
---
# WizardLM: An Instruction-following LLM Using Evol-Instruct
These files are the result of merging the [delta weights](https://huggingface.co/victor123/WizardLM) with the original Llama7B model.
The code for merging is provided in the [WizardLM official Github repo](https://github.com/nlpxucan/WizardLM).
## WizardLM-7B GGML
This repo contains GGML files for for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).
## Other repositories available
* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GPTQ)
* [Unquantised model in HF format](https://huggingface.co/TheBloke/wizardLM-7B-HF)
## REQUIRES LATEST LLAMA.CPP (May 12th 2023 - commit b9fd7ee)!
llama.cpp recently made a breaking change to its quantisation methods.
I have re-quantised the GGML files in this repo. Therefore you will require llama.cpp compiled on May 12th or later (commit `b9fd7ee` or later) to use them.
The previous files, which will still work in older versions of llama.cpp, can be found in branch `previous_llama`.
## 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.q5_0.bin` | q5_0 | 5bit | 4.63GB | 7GB | Higher quality inference than 4bit, at cost of slightly higher resources. |
`WizardLM-7B.GGML.q5_1.bin` | q5_1 | 5bit | 5.0GB | 7GB | Higher quality and resource usage again. |
## 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](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 llama.cpp quantisation methods (May 12th)
**Thireus** has written a [great guide on how to update it to the latest llama.cpp code](https://huggingface.co/TheBloke/wizardLM-7B-GGML/discussions/5) to get support for thew newer files quicker.
# 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.
 |