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117 lines
4.4 KiB
Markdown
117 lines
4.4 KiB
Markdown
<h1 align="center">GPT4All</h1>
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<p align="center">Demo, data and code to train an assistant-style large language model with ~800k GPT-3.5-Turbo Generations based on LLaMa</p>
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<p align="center">
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<a href="https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf">:green_book: Technical Report</a>
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</p>
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Run on M1 Mac (not sped up!)
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# Try it yourself
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Clone this repository down and download the CPU quantized gpt4all model.
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- [gpt4all-quantized](https://s3.amazonaws.com/static.nomic.ai/gpt4all/models/gpt4all-lora-quantized.bin)
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Place the quantized model in the `chat` directory and start chatting by running:
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- `./chat/gpt4all-lora-quantized-OSX-m1` on M1 Mac/OSX
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- `./chat/gpt4all-lora-quantized-linux-x86` on Windows/Linux
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To compile for custom hardware, see our fork of the [Alpaca C++](https://github.com/zanussbaum/gpt4all.cpp) repo.
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Note: the full model on GPU (16GB of RAM required) performs much better in our qualitative evaluations.
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# Reproducibility
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Trained LoRa Weights:
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- gpt4all-lora: https://huggingface.co/nomic-ai/gpt4all-lora
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- gpt4all-lora-epoch-2 https://huggingface.co/nomic-ai/gpt4all-lora-epoch-2
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Raw Data:
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- [Training Data Without P3](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2022_03_27/gpt4all_curated_data_without_p3_2022_03_27.tar.gz)
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- Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean_without_p3
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- [Full Dataset with P3](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2022_03_27/gpt4all_curated_data_full_2022_03_27.tar.gz)
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- Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean
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We are not distributing a LLaMa 7B checkpoint.
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You can reproduce our trained model by doing the following:
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## Setup
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Clone the repo
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`git clone --recurse-submodules git@github.com:nomic-ai/gpt4all.git`
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`git submodule configure && git submodule update`
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Setup the environment
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```
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python -m pip install -r requirements.txt
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cd transformers
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pip install -e .
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cd ../peft
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pip install -e .
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```
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## Training
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```bash
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accelerate launch --dynamo_backend=inductor --num_processes=8 --num_machines=1 --machine_rank=0 --deepspeed_multinode_launcher standard --mixed_precision=bf16 --use_deepspeed --deepspeed_config_file=configs/deepspeed/ds_config.json train.py --config configs/train/finetune-7b.yaml
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```
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## Generate
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```bash
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python generate.py --config configs/generate/generate.yaml --prompt "Write a script to reverse a string in Python
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```
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# Sample Generations
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### Provide instructions for the given exercise. Leg Raises
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>Leg Raises
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>1. Stand with your feet shoulder-width apart and your knees slightly bent.
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>2. Raise your arms straight out in front of you.
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>3. Slowly bend your knees and raise your heels off the ground.
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>4. Hold this position for a count of 3, then slowly lower your heels back to the ground.
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>5. Repeat the exercise 10 times.
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>6. Do not let your knees go beyond your toes.
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>7. Keep your back straight and your abdominal muscles engaged.
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>8. Do not let your hips sag or your back arch.
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>9. Keep your arms straight and your elbows locked.
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>10. Do not let your head drop or your shoulders rise.
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>11. Breathe normally throughout the exercise.
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>12. Rest for 30 seconds before repeating the exercise.
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>13. Gradually increase the number of repetitions as you become stronger.
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### A color description has been provided. Find the CSS code associated with that color. A light red color with a medium light shade of pink
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>The CSS code associated with the color provided is: #FF6347
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### Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary.
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>1. The title of your movie plot should be "The Last Stand". 2. The summary of your movie plot should be: A group of mercenaries are hired to protect a small town from a dangerous criminal who is on the run. However, the town's sheriff is not willing to let them take over and decides to take matters into his own hands.
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If you utilize this reposistory, models or data in a downstream project, please consider citing it with:
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```
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@misc{gpt4all,
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author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
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title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
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}
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```
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