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TheBloke/gpt4-alpaca-lora-30b-HF
gpt4-alpaca-lora-30b-HF is a text generation model from TheBloke. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
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From the Hugging Face model README
This is a pre-merged 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.
You will need at least 60GB VRAM to use this model.
For a GPTQ quantized 4bit model, usable on a 24GB GPU, see: GPT4-Alpaca-LoRA-30B-GPTQ-4bit-128g
<!-- footer start -->For further support, and discussions on these models and AI in general, join us at:
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 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!
<!-- footer end -->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.
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.