README.md

metadata

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

VicUnlocked-30B-LoRA GPTQ

This repo contains a GPTQ format quantised 4bit model for Neko Institute of Science's VicUnLocked 30B LoRA.

The files in this repo are the result of merging the above LoRA with the original LLaMA 30B, then quantising to 4bit using GPTQ-for-LLaMa.

Repositories available

• 4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference.
• 4bit's GPTQ 4-bit model for GPU inference.
• float16 HF format model for GPU inference and further conversions.

How to easily download and use this model in text-generation-webui

Open the text-generation-webui UI as normal.

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/VicUnlocked-30B-LoRA-GPTQ.
3. Click Download.
4. Wait until it says it's finished downloading.
5. Click the Refresh icon next to Model in the top left.
6. In the Model drop-down: choose the model you just downloaded, VicUnlocked-30B-LoRA-GPTQ.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the GPTQ parameters on the right: Bits = 4, Groupsize = None, model_type = Llama
9. Click Save settings for this model in the top right.
10. Click Reload the Model in the top right.
11. Once it says it's loaded, click the Text Generation tab and enter a prompt!

Provided files

Compatible file - VicUnlocked-30B-LoRA-GPTQ-4bit.act-order.safetensors

In the main branch - the default one - you will find VicUnlocked-30B-LoRA-GPTQ-4bit.act-order.safetensors

This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility

It was created without groupsize so as to minimise VRAM requirements. It is created with the --act-order parameter to improve inference quality.

• VicUnlocked-30B-LoRA-GPTQ-4bit-128g.compat.no-act-order.safetensors
  • Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
  • Works with AutoGPTQ.
  • Works with text-generation-webui one-click-installers
  • Parameters: Groupsize = None. act-order.
  • Command used to create the GPTQ:

    llama.py /workspace/vicunlocked-30b/HF wikitext2 --wbits 4 --true-sequential --act-order   --save_safetensors /workspace/vicunlocked-30b/gptq/VicUnlocked-30B-GPTQ-4bit.act-order.safetensors
    

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