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--- |
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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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inference: false |
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--- |
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# Wizard-Vicuna-13B-GGML |
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This is GGML format quantised 4bit and 5bit models of [junelee's wizard-vicuna 13B](https://huggingface.co/junelee/wizard-vicuna-13b). |
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It is the result of quantising to 4bit and 5bit GGML for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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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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## REQUIRES LATEST LLAMA.CPP (May 12th 2023 - commit b9fd7ee)! |
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llama.cpp recently made a breaking change to its quantisation methods. |
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I have re-quantised the GGML files in this repo. Therefore you will require llama.cpp compiled on May 12th or later (commit `b9fd7ee` or later) to use them. |
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The previous files, which will still work in older versions of llama.cpp, can be found in branch `previous_llama`. |
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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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`wizard-vicuna-13B.ggml.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | 4-bit. | |
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`wizard-vicuna-13B.ggml.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | |
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`wizard-vicuna-13B.ggml.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | |
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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 wizard-vicuna-13B.ggml.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### 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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## How to run in `text-generation-webui` |
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GGML models can be loaded into text-generation-webui by installing the llama.cpp module, then placing the ggml model file in a model folder as usual. |
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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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Note: at this time text-generation-webui may not support the new May 12th llama.cpp quantisation methods. |
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**Thireus** has written a [great guide on how to update it to the latest llama.cpp code](https://huggingface.co/TheBloke/wizardLM-7B-GGML/discussions/5) which may be useful to get text-gen-ui working with the new llama.cpp quant methods sooner. |
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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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