---
datasets:
- gozfarb/ShareGPT_Vicuna_unfiltered
license: other
inference: false
---

# 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).


# 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