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language: |
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- en |
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tags: |
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- causal-lm |
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- llama |
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--- |
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# Wizard-Vicuna-13B-HF |
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This is a float16 HF format repo for [junelee's wizard-vicuna 13B](https://huggingface.co/junelee/wizard-vicuna-13b). |
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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. |
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This model was converted to float16 to make it easier to load and manage. |
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## Repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GPTQ). |
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* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-GGML). |
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* [float16 HF format model for GPU inference](https://huggingface.co/TheBloke/wizard-vicuna-13B-HF). |
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# Original WizardVicuna-13B model card |
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Github page: https://github.com/melodysdreamj/WizardVicunaLM |
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# WizardVicunaLM |
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### Wizard's dataset + ChatGPT's conversation extension + Vicuna's tuning method |
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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. |
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## Benchmark |
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### Approximately 7% performance improvement over VicunaLM |
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### Detail |
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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. |
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| | gpt3.5 | wizard-vicuna-13b | vicuna-13b | wizard-7b | link | |
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|-----|--------|-------------------|------------|-----------|----------| |
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| Q1 | 95 | 90 | 85 | 88 | [link](https://sharegpt.com/c/YdhIlby) | |
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| Q2 | 95 | 97 | 90 | 89 | [link](https://sharegpt.com/c/YOqOV4g) | |
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| Q3 | 85 | 90 | 80 | 65 | [link](https://sharegpt.com/c/uDmrcL9) | |
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| Q4 | 90 | 85 | 80 | 75 | [link](https://sharegpt.com/c/XBbK5MZ) | |
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| Q5 | 90 | 85 | 80 | 75 | [link](https://sharegpt.com/c/AQ5tgQX) | |
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| Q6 | 92 | 85 | 87 | 88 | [link](https://sharegpt.com/c/eVYwfIr) | |
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| Q7 | 95 | 90 | 85 | 92 | [link](https://sharegpt.com/c/Kqyeub4) | |
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| Q8 | 90 | 85 | 75 | 70 | [link](https://sharegpt.com/c/M0gIjMF) | |
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| Q9 | 92 | 85 | 70 | 60 | [link](https://sharegpt.com/c/fOvMtQt) | |
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| Q10 | 90 | 80 | 75 | 85 | [link](https://sharegpt.com/c/YYiCaUz) | |
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| Q11 | 90 | 85 | 75 | 65 | [link](https://sharegpt.com/c/HMkKKGU) | |
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| Q12 | 85 | 90 | 80 | 88 | [link](https://sharegpt.com/c/XbW6jgB) | |
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| Q13 | 90 | 95 | 88 | 85 | [link](https://sharegpt.com/c/JXZb7y6) | |
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| Q14 | 94 | 89 | 90 | 91 | [link](https://sharegpt.com/c/cTXH4IS) | |
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| Q15 | 90 | 85 | 88 | 87 | [link](https://sharegpt.com/c/GZiM0Yt) | |
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| | 91 | 88 | 82 | 80 | | |
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## Principle |
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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. |
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Turning a single command into a rich conversation is what we've done [here](https://sharegpt.com/c/6cmxqq0). |
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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). |
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## Detailed Method |
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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. |
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After that, we applied the following model using Vicuna's fine-tuning format. |
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## Training Process |
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Trained with 8 A100 GPUs for 35 hours. |
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## Weights |
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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. |
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## Conclusion |
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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. |
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## License |
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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. |
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## Author |
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[JUNE LEE](https://github.com/melodysdreamj) - He is active in Songdo Artificial Intelligence Study and GDG Songdo. |
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