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--- |
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license: other |
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inference: false |
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--- |
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# WizardLM: An Instruction-following LLM Using Evol-Instruct |
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These files are the result of merging the [delta weights](https://huggingface.co/victor123/WizardLM) with the original Llama7B model. |
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The code for merging is provided in the [WizardLM official Github repo](https://github.com/nlpxucan/WizardLM). |
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## WizardLM-7B GGML |
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This repo contains GGML files for for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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## Other repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GPTQ) |
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* [Unquantised model in HF format](https://huggingface.co/TheBloke/wizardLM-7B-HF) |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`WizardLM-7B.GGML.q4_0.bin` | q4_0 | 4bit | 4.2GB | 6GB | Maximum compatibility | |
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`WizardLM-7B.GGML.q4_2.bin` | q4_2 | 4bit | 4.2GB | 6GB | Best compromise between resources, speed and quality | |
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`WizardLM-7B.GGML.q4_3.bin` | q4_3 | 4bit | 5.0GB | 7GB | Maximum quality 4bit, higher RAM requirements and slower inference | |
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`WizardLM-7B.GGML.q5_0.bin` | q5_0 | 5bit | 4.63GB | 7GB | Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources. | |
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`WizardLM-7B.GGML.q5_1.bin` | q5_1 | 5bit | 5.0GB | 7GB | Brand new 5bit method. Slightly higher resource usage than q5_0.| |
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* The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp |
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* 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. |
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* The q4_3 file offers the highest quality, at the cost of increased RAM usage and slower inference speed. This format is still subject to change and there may be compatibility issues, see below. |
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* The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0. |
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* The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1. |
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## q4_2 and q4_3 compatibility |
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q4_2 and q4_3 are new 4bit quantisation methods offering improved quality. However they are still under development and their formats are subject to change. |
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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. |
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If and when the q4_2 and q4_3 files no longer work with recent versions of llama.cpp I will endeavour to update them. |
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If you want to ensure guaranteed compatibility with a wide range of llama.cpp versions, use the q4_0 file. |
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## q5_0 and q5_1 compatibility |
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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. |
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Don't expect any third-party UIs/tools to support them yet. |
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## How to run in `llama.cpp` |
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I use the following command line; adjust for your tastes and needs: |
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``` |
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./main -t 18 -m WizardLM-7B.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. |
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### Instruction: |
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Write a story about llamas |
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### Response:" |
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``` |
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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`. |
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` |
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## How to run in `text-generation-webui` |
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Put the desired .bin file in a model directory with `ggml` (case sensitive) in its name. |
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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). |
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# Original model info |
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Overview of Evol-Instruct |
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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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