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Model: TheBloke/wizard-vicuna-13B-HF
Source: Original Platform

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README.md

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+---
+language:
+  - en
+tags:
+  - causal-lm
+  - llama
+---
+<!-- header start -->
+<div style="width: 100%;">
+    <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
+    </div>
+    <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
+    </div>
+</div>
+<!-- header end -->
+# Wizard-Vicuna-13B-HF
+
+This is a float16 HF format repo for [junelee's wizard-vicuna 13B](https://huggingface.co/junelee/wizard-vicuna-13b).
+
+June Lee's repo was also HF format. The reason I've made this is that the original repo was in float32, meaning it required 52GB disk space, VRAM and RAM.
+
+This model was converted to float16 to make it easier to load and manage.
+
+## Repositories available
+
+* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GPTQ).
+* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GGML).
+* [float16 HF format model for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-HF).
+
+<!-- footer start -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+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.
+
+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.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**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.
+
+Thank you to all my generous patrons and donaters!
+<!-- footer end -->
+
+# Original WizardVicuna-13B model card
+
+Github page: https://github.com/melodysdreamj/WizardVicunaLM
+
+# WizardVicunaLM
+### Wizard's dataset + ChatGPT's conversation extension + Vicuna's tuning method
+I am a big fan of the ideas behind WizardLM and VicunaLM. I particularly like the idea of WizardLM handling the dataset itself more deeply and broadly, as well as VicunaLM overcoming the limitations of single-turn conversations by introducing multi-round conversations. As a result, I combined these two ideas to create WizardVicunaLM. This project is highly experimental and designed for proof of concept, not for actual usage.
+
+
+## Benchmark
+### Approximately 7% performance improvement over VicunaLM
+![](https://user-images.githubusercontent.com/21379657/236088663-3fa212c9-0112-4d44-9b01-f16ea093cb67.png)
+
+
+### Detail
+
+The questions presented here are not from rigorous tests, but rather, I asked a few questions and requested GPT-4 to score them. The models compared were ChatGPT 3.5, WizardVicunaLM, VicunaLM, and WizardLM, in that order.
+
+|     | gpt3.5 | wizard-vicuna-13b | vicuna-13b | wizard-7b | link     |
+|-----|--------|-------------------|------------|-----------|----------|
+| Q1  | 95     | 90                | 85         | 88        | [link](https://sharegpt.com/c/YdhIlby) |
+| Q2  | 95     | 97                | 90         | 89        | [link](https://sharegpt.com/c/YOqOV4g) |
+| Q3  | 85     | 90                | 80         | 65        | [link](https://sharegpt.com/c/uDmrcL9) |
+| Q4  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/XBbK5MZ) |
+| Q5  | 90     | 85                | 80         | 75        | [link](https://sharegpt.com/c/AQ5tgQX) |
+| Q6  | 92     | 85                | 87         | 88        | [link](https://sharegpt.com/c/eVYwfIr) |
+| Q7  | 95     | 90                | 85         | 92        | [link](https://sharegpt.com/c/Kqyeub4) |
+| Q8  | 90     | 85                | 75         | 70        | [link](https://sharegpt.com/c/M0gIjMF) |
+| Q9  | 92     | 85                | 70         | 60        | [link](https://sharegpt.com/c/fOvMtQt) |
+| Q10 | 90     | 80                | 75         | 85        | [link](https://sharegpt.com/c/YYiCaUz) |
+| Q11 | 90     | 85                | 75         | 65        | [link](https://sharegpt.com/c/HMkKKGU) |
+| Q12 | 85     | 90                | 80         | 88        | [link](https://sharegpt.com/c/XbW6jgB) |
+| Q13 | 90     | 95                | 88         | 85        | [link](https://sharegpt.com/c/JXZb7y6) |
+| Q14 | 94     | 89                | 90         | 91        | [link](https://sharegpt.com/c/cTXH4IS) |
+| Q15 | 90     | 85                | 88         | 87        | [link](https://sharegpt.com/c/GZiM0Yt) |
+|     | 91     | 88                | 82         | 80        |          |
+
+
+## Principle
+
+We adopted the approach of WizardLM, which is to extend a single problem more in-depth. However, instead of using individual instructions, we expanded it using Vicuna's conversation format and applied Vicuna's fine-tuning techniques.
+
+Turning a single command into a rich conversation is what we've done [here](https://sharegpt.com/c/6cmxqq0).
+
+After creating the training data, I later trained it according to the Vicuna v1.1 [training method](https://github.com/lm-sys/FastChat/blob/main/scripts/train_vicuna_13b.sh).
+
+
+## Detailed Method
+
+First, we explore and expand various areas in the same topic using the 7K conversations created by WizardLM. However, we made it in a continuous conversation format instead of the instruction format. That is, it starts with WizardLM's instruction, and then expands into various areas in one conversation using ChatGPT 3.5.
+
+After that, we applied the following model using Vicuna's fine-tuning format.
+
+## Training Process
+
+Trained with 8 A100 GPUs for 35 hours.
+
+## Weights
+You can see the [dataset](https://huggingface.co/datasets/junelee/wizard_vicuna_70k) we used for training and the [13b model](https://huggingface.co/junelee/wizard-vicuna-13b) in the huggingface.
+
+## Conclusion
+If we extend the conversation to gpt4 32K, we can expect a dramatic improvement, as we can generate 8x more, more accurate and richer conversations.
+
+## License
+The model is licensed under the LLaMA model, and the dataset is licensed under the terms of OpenAI because it uses ChatGPT. Everything else is free.
+
+## Author
+
+[JUNE LEE](https://github.com/melodysdreamj) - He is active in Songdo Artificial Intelligence Study and GDG Songdo.

config.json

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+  ],
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+  "vocab_size": 32000
+}

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special_tokens_map.json

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+    "single_word": false
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+}

tokenizer_config.json

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