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license: other
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# 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).
The original WizardLM deltas are in float32, and this results in producing an HF repo that is also float32, and so much larger than a normal 7B Llama model.
Therefore for this repo I converted the model to float16, to produce a standard size 7B model.
This was done achieved by running **`model = model.half()`** prior to saving.
## WizardLM-7B HF
This repo contains the full unquantised model files in HF format for GPU inference and as a base for quantisation/conversion.
## Other repositories available
* [4bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GGML)
* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GPTQ)
# Original model info
## Full details in the model's Github page
[WizardLM official Github repo](https://github.com/nlpxucan/WizardLM).
## 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.
Although on our complexity-balanced test set, WizardLM-7B outperforms ChatGPT in the high-complexity instructions, it still lag behind ChatGPT on the entire test set, and we also consider WizardLM to still be in a baby state. This repository will continue to improve WizardLM, train on larger scales, add more training data, and innovate more advanced large-model training methods.
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