---
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](https://huggingface.co/chansung/gpt4-alpaca-lora-30b).

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](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF)

The files in this repo were then quantized to 4bit for use with [llama.cpp](https://github.com/ggerganov/llama.cpp) using the new 4bit quantisation methods being worked on in [PR #896](https://github.com/ggerganov/llama.cpp/pull/896).

## Provided files

Two files are provided. One is quantised using method Q4_0, the other in Q4_1.

The Q4_1 file requires more RAM and may run a little slower. It may give slightly better results, but this is not proven.

## 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_1.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`

# 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](https://github.com/tloen/alpaca-lora) implementation
- Training script:
```shell
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](https://wandb.ai/chansung18/gpt4_alpaca_lora/runs/w3syd157?workspace=user-chansung18).