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# GPT4 Alpaca LoRA 30B - 4bit GGML

This is a 4-bit GGML version of the Chansung GPT4 Alpaca 30B LoRA model.

It was created by merging the LoRA provided in the above repo with the original Llama 30B model, producing unquantised model GPT4-Alpaca-LoRA-30B-HF

The files in this repo were then quantized to 4bit and 5bit for use with llama.cpp.

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.

For files compatible with the previous version of llama.cpp, please see branch previous_llama_ggmlv2.

Provided files

Name	Quant method	Bits	Size	RAM required	Use case
`gpt4-alpaca-lora-30B.ggmlv3.q4_0.bin`	q4_0	4bit	20.3GB	23GB	4bit.
`gpt4-alpaca-lora-30B.ggmlv3.q4_1.bin`	q4_1	4bit	22.4GB	25GB	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
`gpt4-alpaca-lora-30B.ggmlv3.q5_0.bin`	q5_0	5bit	22.4GB	25GB	5bit. Higher accuracy, higher resource usage, slower inference.
`gpt4-alpaca-lora-30B.ggmlv3.q5_1.bin`	q5_1	5bit	24.4GB	27GB	5bit. Even higher accuracy and resource usage, and slower inference.

How to run in `llama.cpp`

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

./main -t 18 -m gpt4-alpaca-lora-30B.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a story about llamas
### Response:"

Change -t 18 to the number of physical CPU cores you have. For example if your system has 6 cores/12 threads, use -t 6.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

How to run in `text-generation-webui`

Create a model directory that has ggml (case sensitive) in its name. Then put the desired .bin file in that model directory.

Further instructions here: text-generation-webui/docs/llama.cpp-models.md.

Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files.

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 GPT4 Alpaca Lora model card

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.

Training script: borrowed from the official Alpaca-LoRA implementation
Training script:

python finetune.py \
    --base_model='decapoda-research/llama-30b-hf' \
    --data_path='alpaca_data_gpt4.json' \
    --num_epochs=10 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir='./gpt4-alpaca-lora-30b' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \
    --batch_size=... \
    --micro_batch_size=...

You can find how the training went from W&B report here.