Demo, data and code to train an assistant-style large language model with ~800k GPT-3.5-Turbo Generations based on LLaMa
 Run on M1 Mac (not sped up!) # Try it yourself 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) Clone this repository down and place the quantized model in the `chat` directory and start chatting by running: - `cd chat;./gpt4all-lora-quantized-OSX-m1` on M1 Mac/OSX - `cd chat;./gpt4all-lora-quantized-linux-x86` on Linux - `cd chat;./gpt4all-lora-quantized-win64.exe` on Windows (PowerShell) - `cd chat;./gpt4all-lora-quantized-OSX-intel` on Intel Mac/OSX To compile for custom hardware, see our fork of the [Alpaca C++](https://github.com/zanussbaum/gpt4all.cpp) repo. ----------- [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) This model had all refusal to answer responses removed from training. Try it with: - `cd chat;./gpt4all-lora-quantized-OSX-m1 -m gpt4all-lora-unfiltered-quantized.bin` ----------- Note: the full model on GPU (16GB of RAM required) performs much better in our qualitative evaluations. # Reproducibility Trained LoRa Weights: - gpt4all-lora (four full epochs of training): https://huggingface.co/nomic-ai/gpt4all-lora - gpt4all-lora-epoch-2 (three full epochs of training) https://huggingface.co/nomic-ai/gpt4all-lora-epoch-2 Raw Data: - [Training Data Without P3](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations) - Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean_without_p3 - [Full Dataset with P3](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2022_03_27/gpt4all_curated_data_full_2022_03_27.tar.gz) - Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean We are not distributing a LLaMa 7B checkpoint. You can reproduce our trained model by doing the following: ## Setup Clone the repo `git clone --recurse-submodules https://github.com/nomic-ai/gpt4all.git` `git submodule configure && git submodule update` Setup the environment ``` python -m pip install -r requirements.txt cd transformers pip install -e . cd ../peft pip install -e . ``` ## Training ```bash 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 ``` ## Generate ```bash python generate.py --config configs/generate/generate.yaml --prompt "Write a script to reverse a string in Python" ``` # Sample Generations ### Provide instructions for the given exercise. Leg Raises >Leg Raises >1. Stand with your feet shoulder-width apart and your knees slightly bent. >2. Raise your arms straight out in front of you. >3. Slowly bend your knees and raise your heels off the ground. >4. Hold this position for a count of 3, then slowly lower your heels back to the ground. >5. Repeat the exercise 10 times. >6. Do not let your knees go beyond your toes. >7. Keep your back straight and your abdominal muscles engaged. >8. Do not let your hips sag or your back arch. >9. Keep your arms straight and your elbows locked. >10. Do not let your head drop or your shoulders rise. >11. Breathe normally throughout the exercise. >12. Rest for 30 seconds before repeating the exercise. >13. Gradually increase the number of repetitions as you become stronger. ### 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 >The CSS code associated with the color provided is: #FF6347 ### Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. >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. ### Reverse a string in python. > 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: > ```python > my_string = "Hello World" # Define your original string here > reversed_str = my_string[::-1] # Use a slice and negative index for reverse order > print(reversed_str) # Output will be reversed version of the inputted string. ### List 10 dogs. >1. Labrador Retriever >2. Golden Retriever >3. Beagle >4. Poodle >5. German Shepherd >6. Bulldog >7. Rottweiler >8. Boxer >9. Shih Tzu >10. Pug ### Write me a poem about the fall of Julius Ceasar into a ceasar salad in iambic pentameter. >The fall of Julius Ceasar into a ceasar salad >In iambic pentameter, >The mighty Roman emperor >Fell into a salad of lettuce and croutons, >His empire crumbling around him, >As he was devoured by the hungry diners. >The once mighty emperor >Was now just a salad topping, >His legacy reduced to a mere garnish, >As he was consumed by the masses. >The fall of Julius Ceasar into a ceasar salad, >In iambic pentameter, >Was a tragic end to a great man, >Who had once ruled the world with an iron fist. >But now, he was just a salad topping, >His legacy reduced to a mere garnish, >As he was consumed by the masses. ### What is a three word topic describing the following keywords: baseball, football, soccer: >Sports, athletics, games ### GPU Interface There are two ways to get up and running with this model on GPU. 1. clone the nomic client [repo](https://github.com/nomic-ai/nomic) and run `pip install .[GPT4All]` in the home dir. 2. run `pip install nomic` and install the additional deps from the wheels built [here](https://github.com/nomic-ai/nomic/tree/main/bin) Once this is done, you can run the model on GPU with a script like the following: ``` from nomic import GPT4AllGPU m = GPT4AllGPU(LLAMA_PATH) config = {'num_beams': 2, 'min_new_tokens': 10, 'max_length': 100, 'repetition_penalty': 2.0} out = m.generate('write me a story about a lonely computer', config) print(out) ``` You can pass any of the [huggingface generation config params](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) in the config. If you utilize this reposistory, models or data in a downstream project, please consider citing it with: ``` @misc{gpt4all, author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar}, title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/nomic-ai/gpt4all}}, } ```