README.md

---
datasets:
- gozfarb/ShareGPT_Vicuna_unfiltered
license: other
inference: false
---
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# VicUnlocked-30B-LoRA GGML

This is GGML format quantised 4-bit, 5-bit and 8-bit models of [Neko Institute of Science's VicUnLocked 30B LoRA](https://huggingface.co./Neko-Institute-of-Science/VicUnLocked-30b-LoRA).

The files in this repo are the result of merging the above LoRA with the original LLaMA 30B, then converting to GGML for CPU (+ CUDA) inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).

## Repositories available

* [4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co./TheBloke/VicUnlocked-30B-LoRA-GGML).
* [4-bit GPTQ model for GPU inference](https://huggingface.co./TheBloke/VicUnlocked-30B-LoRA-GPTQ).
* [float16 HF format model for GPU inference and further conversions](https://huggingface.co./TheBloke/VicUnlocked-30B-LoRA-HF).

## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!

llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508

I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them.

For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`.
## Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
`VicUnlocked-30B-LoRA.ggmlv3.q4_0.bin` | q4_0 | 4bit | 20.3GB | 23GB | 4-bit. |
`VicUnlocked-30B-LoRA.ggmlv3.q4_1.bin` | q4_1 | 5bit | 24.4GB | 27GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
`VicUnlocked-30B-LoRA.ggmlv3.q5_0.bin` | q5_0 | 5bit | 22.4GB | 25GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
`VicUnlocked-30B-LoRA.ggmlv3.q5_1.bin` | q5_1 | 5bit | 24.4GB | 27GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. |
`VicUnlocked-30B-LoRA.ggmlv3.q8_0.bin` | q8_0 | 8bit | 36.6GB | 39GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |

## How to run in `llama.cpp`

I use the following command line; adjust for your tastes and needs:

```
./main -t 8 -m VicUnlocked-30B-LoRA.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: write a story about llamas ### Response:"
```

Change `-t 8` to the number of physical CPU cores you have.

## 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).

## Want to support my work?

I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills.

So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects.

Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try.

* Patreon: coming soon! (just awaiting approval)
* Ko-Fi: https://ko-fi.com/TheBlokeAI
* Discord: https://discord.gg/UBgz4VXf
# Original model card

# Convert tools
https://github.com/practicaldreamer/vicuna_to_alpaca

# Training tool
https://github.com/oobabooga/text-generation-webui

ATM I'm using 2023.05.04v0 of the dataset and training full context.

# Notes:
So I will only be training 1 epoch, as full context 30b takes so long to train.
This 1 epoch will take me 8 days lol but luckily these LoRA feels fully functinal at epoch 1 as shown on my 13b one.
Also I will be uploading checkpoints almost everyday. I could train another epoch if there's enough want for it.

Update: Since I will not be training over 1 epoch @Aeala is training for the full 3 https://huggingface.co./Aeala/VicUnlocked-alpaca-half-30b-LoRA but it's half ctx if you care about that. Also @Aeala's just about done.

Update: Training Finished at Epoch 1, These 8 days sure felt long. I only have one A6000 lads there's only so much I can do. Also RIP gozfarb IDK what happened to him.

# How to test?
1. Download LLaMA-30B-HF if you have not: https://huggingface.co./Neko-Institute-of-Science/LLaMA-30B-HF
2. Make a folder called VicUnLocked-30b-LoRA in the loras folder.
3. Download adapter_config.json and adapter_model.bin into VicUnLocked-30b-LoRA.
4. Load ooba: ```python server.py --listen --model LLaMA-30B-HF --load-in-8bit --chat --lora VicUnLocked-30b-LoRA```
5. Select instruct and chose Vicuna-v1.1 template.


# Training Log
https://wandb.ai/neko-science/VicUnLocked/runs/vx8yzwi7