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--- |
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datasets: |
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- gozfarb/ShareGPT_Vicuna_unfiltered |
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license: other |
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inference: false |
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--- |
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# VicUnlocked-30B-LoRA GGML |
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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). |
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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). |
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## Repositories available |
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* [4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/VicUnlocked-30B-LoRA-GGML). |
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* [4-bit GPTQ model for GPU inference](https://huggingface.co/TheBloke/VicUnlocked-30B-LoRA-GPTQ). |
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* [float16 HF format model for GPU inference and further conversions](https://huggingface.co/TheBloke/VicUnlocked-30B-LoRA-HF). |
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)! |
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llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508 |
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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. |
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For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`. |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`VicUnlocked-30B-LoRA.ggmlv3.q4_0.bin` | q4_0 | 4bit | 20.3GB | 23GB | 4-bit. | |
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`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. | |
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`VicUnlocked-30B-LoRA.ggmlv3.q5_0.bin` | q5_0 | 5bit | 22.4GB | 25GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | |
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`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. | |
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`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. | |
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## How to run in `llama.cpp` |
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I use the following command line; adjust for your tastes and needs: |
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``` |
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./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:" |
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``` |
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Change `-t 8` to the number of physical CPU cores you have. |
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## How to run in `text-generation-webui` |
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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. |
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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). |
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# Original model card |
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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 |
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