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README.md
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---
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license: other
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---
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---
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license: other
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language:
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- en
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pipeline_tag: text2text-generation
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tags:
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- alpaca
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- llama
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- chat
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- gpt4
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This is a 4bit 128g GPTQ of [chansung's gpt4-alpaca-lora-13b](https://huggingface.co/chansung/gpt4-alpaca-lora-13b).
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More details will be put in this README tomorrow. Until then, please see one of my other GPTQ repos for more instructions.
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Command to create was:
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```
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cd gptq-safe && CUDA_VISIBLE_DEVICES=0 python3 llama.py /content/gpt4-alpaca-lora-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors /content/gpt4-alpaca-lora-13B-GPTQ-4bit-128g.safetensors
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```
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Note that only as `--act-order` was used, this will not work with ooba's fork of GPTQ. You must use the qwopqwop repo as of April 13th.
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Command to clone the correct GPTQ-for-LLaMa repo for inference using `llama_inference.py`, or in `text-generation-webui`:
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```
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git clone -n https://github.com/qwopqwop200/GPTQ-for-LLaMa gptq-safe
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cd gptq-safe
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git checkout 58c8ab4c7aaccc50f507fd08cce941976affe5e0
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```
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Tomorrow I will also do a `no-act-order.pt` which doesn't use `--act-order` and will therefore work with ooba's GPTQ fork.
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# Original model card is below
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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.
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- Training script: borrowed from the official [Alpaca-LoRA](https://github.com/tloen/alpaca-lora) implementation
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- Training script:
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```shell
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python finetune.py \
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--base_model='decapoda-research/llama-30b-hf' \
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--data_path='alpaca_data_gpt4.json' \
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--num_epochs=10 \
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--cutoff_len=512 \
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--group_by_length \
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--output_dir='./gpt4-alpaca-lora-30b' \
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--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
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--lora_r=16 \
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--batch_size=... \
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--micro_batch_size=...
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```
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You can find how the training went from W&B report [here](https://wandb.ai/chansung18/gpt4_alpaca_lora/runs/w3syd157?workspace=user-chansung18).
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