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Model: TheBloke/wizardLM-7B-HF Source: Original Platform
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README.md
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license: other
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<!-- header start -->
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<div style="width: 100%;">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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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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The original WizardLM deltas are in float32, and this results in producing an HF repo that is also float32, and is much larger than a normal 7B Llama model.
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Therefore for this repo I converted the merged model to float16, to produce a standard size 7B model.
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This was achieved by running **`model = model.half()`** prior to saving.
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## WizardLM-7B HF
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This repo contains the full unquantised model files in HF format for GPU inference and as a base for quantisation/conversion.
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## Other repositories available
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* [4bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GGML)
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* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizardLM-7B-GPTQ)
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
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## Thanks, and how to contribute.
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Thanks to the [chirper.ai](https://chirper.ai) team!
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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.
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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.
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**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.
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Thank you to all my generous patrons and donaters!
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<!-- footer end -->
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# Original model info
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## Full details in the model's Github page
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[WizardLM official Github repo](https://github.com/nlpxucan/WizardLM).
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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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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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