# GPT4 Alpaca LoRA 30B HF
This is a pre-merged 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.
You will need at least 60GB VRAM to use this model.
For a [GPTQ](https://github.com/qwopqwop200/GPTQ-for-LLaMa) quantized 4bit model, usable on a 24GB GPU, see: [GPT4-Alpaca-LoRA-30B-GPTQ-4bit-128g](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30B-GPTQ-4bit-128g)
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://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](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).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 51.93 |
| ARC (25-shot) | 64.85 |
| HellaSwag (10-shot) | 85.72 |
| MMLU (5-shot) | 58.51 |
| TruthfulQA (0-shot) | 52.24 |
| Winogrande (5-shot) | 80.19 |
| GSM8K (5-shot) | 15.54 |
| DROP (3-shot) | 6.44 |
