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--- |
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datasets: |
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- gozfarb/ShareGPT_Vicuna_unfiltered |
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- Aeala/ShareGPT_Vicuna_unfiltered |
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tags: |
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- uncensored |
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--- |
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# Convert tools |
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https://github.com/practicaldreamer/vicuna_to_alpaca |
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# Training tool |
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https://github.com/oobabooga/text-generation-webui |
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ATM I'm using 2023.05.04v0 of the dataset and training full context. |
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# Notes: |
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So I will only be training 1 epoch, as full context 30b takes so long to train. |
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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. |
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Also I will be uploading checkpoints almost everyday. I could train another epoch if there's enough want for it. |
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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. |
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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. |
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# How to test? |
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1. Download LLaMA-30B-HF if you have not: https://huggingface.co/Neko-Institute-of-Science/LLaMA-30B-HF |
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2. Make a folder called VicUnLocked-30b-LoRA in the loras folder. |
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3. Download adapter_config.json and adapter_model.bin into VicUnLocked-30b-LoRA. |
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4. Load ooba: ```python server.py --listen --model LLaMA-30B-HF --load-in-8bit --chat --lora VicUnLocked-30b-LoRA``` |
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5. Select instruct and chose Vicuna-v1.1 template. |
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# Training Log |
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https://wandb.ai/neko-science/VicUnLocked/runs/vx8yzwi7 |
