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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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inference: false |
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--- |
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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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## How to easily download and use this model in text-generation-webui |
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Open the text-generation-webui UI as normal. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/gpt4-alpaca-lora-13B-GPTQ-4bit-128g`. |
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3. Click **Download**. |
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4. Wait until it says it's finished downloading. |
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5. Click the **Refresh** icon next to **Model** in the top left. |
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6. In the **Model drop-down**: choose the model you just downloaded,`gpt4-alpaca-lora-13B-GPTQ-4bit-128g`. |
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7. If you see an error in the bottom right, ignore it - it's temporary. |
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8. Check that the `GPTQ parameters` are correct on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama` |
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9. Click **Save settings for this model** in the top right. |
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10. Click **Reload the Model** in the top right. |
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11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! |
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Command to create was: |
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``` |
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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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Command to clone the latest Triton GPTQ-for-LLaMa repo for inference using `llama_inference.py`, or in `text-generation-webui`: |
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``` |
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# Clone text-generation-webui, if you don't already have it |
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git clone https://github.com/oobabooga/text-generation-webui |
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# Make a repositories directory |
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mkdir -p text-generation-webui/repositories |
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cd text-generation-webui/repositories |
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# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui |
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git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa |
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``` |
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There is also a `no-act-order.safetensors` file which will work with oobabooga's fork of GPTQ-for-LLaMa; it does not require the latest GPTQ code. |
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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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