Update README.md

README.md

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 ---
-license: other
+language:
+  - en
+tags:
+  - causal-lm
+  - llama
+inference: false
 ---
+# Wizard-Vicuna-13B-GGML
+
+This is GGML format quantised 4bit and 5bit models of [junlee's wizard-vicuna 13B](https://huggingface.co/junelee/wizard-vicuna-13b).
+
+It is the result of quantising to 4bit and 5bit GGML for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).
+
+## Provided files
+| Name | Quant method | Bits | Size | RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+`wizard-vicuna-13B.ggml.q4_0.bin` | q4_0 | 4bit | 8.14GB | 10.5GB | Maximum compatibility |
+`wizard-vicuna-13B.ggml.q4_2.bin` | q4_2 | 4bit | 8.14GB | 10.5GB | Best compromise between resources, speed and quality |
+`wizard-vicuna-13B.ggml.q5_0.bin` | q5_0 | 5bit | 8.95GB | 11.0GB | Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources. |
+`wizard-vicuna-13B.ggml.q5_1.bin` | q5_1 | 5bit | 9.76GB | 12.25GB | Brand new 5bit method. Slightly higher resource usage than q5_0.|
+
+* The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
+* The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
+* The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
+* The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1.
+
+## q4_2 compatibility
+
+q4_2 is a relatively new 4bit quantisation method offering improved quality. However they are still under development and their formats are subject to change.
+
+In order to use these files you will need to use recent llama.cpp code. And it's possible that future updates to llama.cpp could require that these files are re-generated.
+
+If and when the q4_2 file no longer works with recent versions of llama.cpp I will endeavour to update it.
+
+If you want to ensure guaranteed compatibility with a wide range of llama.cpp versions, use the q4_0 file.
+
+## q5_0 and q5_1 compatibility
+
+These new methods were released to llama.cpp on 26th April. You will need to pull the latest llama.cpp code and rebuild to be able to use them.
+
+Don't expect any third-party UIs/tools to support them yet.
+
+## How to run in `llama.cpp`
+
+I use the following command line; adjust for your tastes and needs:
+
+```
+./main -t 18 -m wizard-vicuna-13B.ggml.q4_2.bi --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:"
+```
+
+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`.
+
+## How to run in `text-generation-webui`
+
+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.
+
+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).
+
+Note: at this time text-generation-webui may not support the new q5 quantisation methods.
+
+**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) so that these files can be used in the UI.
+
+# Original wizard-vicuna-13B model card
+
+# 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.