inference: false
license: other
datasets:
- gozfarb/ShareGPT_Vicuna_unfiltered
Aeala's VicUnlocked Alpaca 65B QLoRA GGML
These files are GGML format model files for Aeala's VicUnlocked Alpaca 65B QLoRA.
GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:
Other repositories available
- 4-bit GPTQ models for GPU inference
- 4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference
- Original unquantised fp16 model in HF format
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.
Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
|---|---|---|---|---|---|
| VicUnlocked-Alpaca-65B.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB | 39.23 GB | 4-bit. |
| VicUnlocked-Alpaca-65B.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB | 43.31 GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| VicUnlocked-Alpaca-65B.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB | 47.39 GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
| VicUnlocked-Alpaca-65B.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB | 51.47 GB | 5-bit. Even higher accuracy, resource usage and slower inference. |
| VicUnlocked-Alpaca-65B.ggmlv3.q8_0.bin | q8_0 | 8 | 69.37 GB | 71.87 GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for most use cases. |
q8_0 file requires expansion from archive
Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the q8_0 file in a multi-part ZIP file. The ZIP is not compressed, it is just storing the .bin file in two parts.
To decompress it, please download
VicUnlocked-Alpaca-65B.ggmlv3.q8_0.zipVicUnlocked-Alpaca-65B.ggmlv3.q8_0.z01
and extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use 7zip - the basic unzip tool did not work. Example:
sudo apt update -y && sudo apt install 7zip
7zz x VicUnlocked-Alpaca-65B.ggmlv3.q8_0.zip # Once the q8_0.bin is extracted you can delete the .zip and .z01
How to run in llama.cpp
I use the following command line; adjust for your tastes and needs:
./main -t 10 -ngl 32 -m VicUnlocked-Alpaca-65B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to run in text-generation-webui
Further instructions here: text-generation-webui/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: Aeala's VicUnlocked Alpaca 65B QLoRA
LoRA Info:
Please note that this is a highly experimental LoRA model. It may do some good stuff, it might do some undesirable stuff. Training is paused for now. Feel free to try it!~
Important Note: While this is trained on a cleaned ShareGPT dataset like Vicuna used, this was trained in the Alpaca format, so prompting should be something like:
### Instruction:
<prompt> (without the <>)
### Response:
Current upload: checkpoint of step 1200 in training.
Benchmarks
wikitext2: Coming soon...
ptb-new: Coming soon...
c4-new: Coming soon...