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gpt4-alpaca-lora-30B-GGML

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metadata
license: other
language:
  - en
pipeline_tag: text2text-generation
tags:
  - alpaca
  - llama
  - chat
  - gpt4
inference: false

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.

Provided files

Name Quant method Bits Size RAM required Use case
gpt4-alpaca-lora-30B.GGML.q4_0.bin q4_0 4bit 20.3GB 23GB Maximum compatibility
gpt4-alpaca-lora-30B.GGML.q4_2.bin q4_2 4bit 20.3GB 23GB Best compromise between resources, speed and quality
gpt4-alpaca-lora-30B.GGML.q4_3.bin q4_3 4bit 24.4GB 27GB Maximum quality 4bit, higher RAM requirements and slower inference
gpt4-alpaca-lora-30B.GGML.q5_0.bin q5_0 5bit 22.4GB 25GB Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources.
gpt4-alpaca-lora-30B.GGML.q5_1.bin q5_1 5bit 24.4GB 27GB Brand new 5bit method. Slightly higher resource usage than q5_0.
  • The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
  • The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
  • The q4_3 file offers the highest quality, at the cost of increased RAM usage and slower inference speed. This format is still subject to change and there may be compatibility issues, see below.
  • The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
  • The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1.

q4_2 and q4_3 compatibility

q4_2 and q4_3 are new 4bit quantisation methods offering improved quality. However they are still under development and their formats are subject to change.

In order to use these files you will need to use recent llama.cpp code. And it's possible that future updates to llama.cpp could require that these files are re-generated.

If and when the q4_2 and q4_3 files no longer work with recent versions of llama.cpp I will endeavour to update them.

If you want to ensure guaranteed compatibility with a wide range of llama.cpp versions, use the q4_0 file.

q5_0 and q5_1 compatibility

These new methods were released to llama.cpp on 26th April. You will need to pull the latest llama.cpp code and rebuild to be able to use them.

Don't expect any third-party UIs/tools to support them yet.

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.GGML.q4_2.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 8 cores/16 threads, use -t 8.

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.

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.