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187 lines
7.4 KiB
Markdown
187 lines
7.4 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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<p align="center">
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<a href="https://discord.gg/kvmy6dQB">Discord</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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Download the CPU quantized gpt4all model checkpoint: [gpt4all-lora-quantized.bin](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin) - [[Torrent-Magnet]](https://tinyurl.com/gpt4all-lora-quantized)
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Clone this repository down and place the quantized model in the `chat` directory and start chatting by running:
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- `cd chat;./gpt4all-lora-quantized-OSX-m1` on M1 Mac/OSX
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- `cd chat;./gpt4all-lora-quantized-linux-x86` on Linux
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- `cd chat;./gpt4all-lora-quantized-win64.exe` on Windows (PowerShell)
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- `cd chat;./gpt4all-lora-quantized-OSX-intel` on Intel Mac/OSX
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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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-----------
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[Secret Unfiltered Checkpoint](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-unfiltered-quantized.bin) - [[Torrent]](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-unfiltered-quantized.bin.torrent)
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This model had all refusal to answer responses removed from training. Try it with:
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- `cd chat;./gpt4all-lora-quantized-OSX-m1 -m gpt4all-lora-unfiltered-quantized.bin`
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-----------
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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 (four full epochs of training): https://huggingface.co/nomic-ai/gpt4all-lora
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- gpt4all-lora-epoch-2 (three full epochs of training) 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 https://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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### Reverse a string in python.
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> In Python, you can reverse a list or tuple by using the reversed() function on it. Here's an example of how to use this method with strings:
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> ```python
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> my_string = "Hello World" # Define your original string here
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> reversed_str = my_string[::-1] # Use a slice and negative index for reverse order
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> print(reversed_str) # Output will be reversed version of the inputted string.
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### List 10 dogs.
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>1. Labrador Retriever
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>2. Golden Retriever
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>3. Beagle
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>4. Poodle
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>5. German Shepherd
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>6. Bulldog
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>7. Rottweiler
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>8. Boxer
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>9. Shih Tzu
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>10. Pug
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### Write me a poem about the fall of Julius Ceasar into a ceasar salad in iambic pentameter.
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>The fall of Julius Ceasar into a ceasar salad
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>In iambic pentameter,
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>The mighty Roman emperor
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>Fell into a salad of lettuce and croutons,
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>His empire crumbling around him,
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>As he was devoured by the hungry diners.
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>The once mighty emperor
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>Was now just a salad topping,
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>His legacy reduced to a mere garnish,
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>As he was consumed by the masses.
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>The fall of Julius Ceasar into a ceasar salad,
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>In iambic pentameter,
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>Was a tragic end to a great man,
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>Who had once ruled the world with an iron fist.
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>But now, he was just a salad topping,
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>His legacy reduced to a mere garnish,
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>As he was consumed by the masses.
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### What is a three word topic describing the following keywords: baseball, football, soccer:
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>Sports, athletics, games
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### GPU Interface
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There are two ways to get up and running with this model on GPU.
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1. clone the nomic client [repo](https://github.com/nomic-ai/nomic) and run `pip install .[GPT4All]` in the home dir.
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2. run `pip install nomic` and install the additional deps from the wheels built [here](https://github.com/nomic-ai/nomic/tree/main/bin)
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Once this is done, you can run the model on GPU with a script like the following:
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```
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from nomic import GPT4AllGPU
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m = GPT4AllGPU(LLAMA_PATH)
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config = {'num_beams': 2,
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'min_new_tokens': 10,
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'max_length': 100,
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'repetition_penalty': 2.0}
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out = m.generate('write me a story about a lonely computer', config)
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print(out)
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```
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You can pass any of the [huggingface generation config params](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) in the config.
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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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