README.md

metadata

inference: false
license: other
datasets:
  - yahma/alpaca-cleaned
language:
  - en
pipeline_tag: text2text-generation
tags:
  - alpaca
  - llama
  - chat

Chat & support: my new Discord server

Want to contribute? TheBloke's Patreon page

chansung's Alpaca LoRA 30B GGML

These files are GGML format model files for chansung's Alpaca LoRA 30B.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

• text-generation-webui
• KoboldCpp
• ParisNeo/GPT4All-UI
• llama-cpp-python
• ctransformers

Repositories available

• elinas' 4-bit GPTQ models for GPU inference
• 4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference
• chansung's unquantised fp16 model in pytorch format, for GPU inference and for further conversions

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	Max RAM required	Use case
Alpaca-Lora-30B.ggmlv3.q4_0.bin	q4_0	4	18.30 GB	20.80 GB	4-bit.
Alpaca-Lora-30B.ggmlv3.q4_1.bin	q4_1	4	20.33 GB	22.83 GB	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
Alpaca-Lora-30B.ggmlv3.q5_0.bin	q5_0	5	22.37 GB	24.87 GB	5-bit. Higher accuracy, higher resource usage and slower inference.
Alpaca-Lora-30B.ggmlv3.q5_1.bin	q5_1	5	24.40 GB	26.90 GB	5-bit. Even higher accuracy, resource usage and slower inference.
Alpaca-Lora-30B.ggmlv3.q8_0.bin	q8_0	8	34.56 GB	37.06 GB	8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to run in llama.cpp

I use the following command line; adjust for your tastes and needs:

./main -t 10 -ngl 32 -m Alpaca-Lora-30B.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.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

• Patreon: https://patreon.com/TheBlokeAI
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Original model card: chansung's Alpaca LoRA 30B

This repository comes with LoRA checkpoint to make LLaMA into a chatbot like language model. The checkpoint is the output of instruction following fine-tuning process with the following settings on 8xA100(40G) DGX system.

• Dataset: cleaned-up Alpaca dataset up to 04/06/23
• Training script: borrowed from the official Alpaca-LoRA implementation
• Training script:

python finetune.py \
    --base_model='decapoda-research/llama-30b-hf' \
    --num_epochs=10 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./lora-alpaca' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \
    --batch_size=... \
    --micro_batch_size=...