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<h1 align="center">GPT4All</h1>
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<p align="center">Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa</p>
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<p align="center">
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<a href="https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf">:green_book: Technical Report 2: GPT4All-J </a>
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</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 1: GPT4All</a>
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</p>
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<p align="center">
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<a href="https://github.com/nomic-ai/pyllamacpp">:snake: Official Python Bindings</a>
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</p>
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<p align="center">
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<a href="https://github.com/nomic-ai/gpt4all-ts">:computer: Official Typescript Bindings</a>
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</p>
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<p align="center">
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<a href="https://github.com/nomic-ai/gpt4all-ui">:speech_balloon: Official Web Chat Interface</a>
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</p>
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<p align="center">
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<a href="https://github.com/nomic-ai/gpt4all-chat">:speech_balloon: Official Chat Interface</a>
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</p>
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<p align="center">
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<a href="https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html">🦜️🔗 Official Langchain Backend</a>
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</p>
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<p align="center">
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<a href="https://discord.gg/mGZE39AS3e">Discord</a>
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</p>
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<p align="center">
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GPT4All is made possible by our compute partner <a href="https://www.paperspace.com/">Paperspace</a>.
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</p>
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## GPT4All-J: An Apache-2 Licensed GPT4All Model
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Run on an M1 Mac (not sped up!)
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### GPT4All-J Chat UI Installers
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Installs a native chat-client with auto-update functionality that runs on your desktop with the GPT4All-J model baked into it.
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[Mac/OSX](https://gpt4all.io/installers/gpt4all-installer-darwin.dmg)
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[Windows](https://gpt4all.io/installers/gpt4all-installer-win64.exe)
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[Ubuntu](https://gpt4all.io/installers/gpt4all-installer-linux.run)
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If you have older hardware that only supports avx and not avx2 you can use these.
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[Mac/OSX - avx-only](https://gpt4all.io/installers/gpt4all-installer-darwin-avx-only.dmg)
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[Windows - avx-only](https://gpt4all.io/installers/gpt4all-installer-win64-avx-only.exe)
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[Ubuntu - avx-only](https://gpt4all.io/installers/gpt4all-installer-linux-avx-only.run)
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These files are not yet cert signed by Windows/Apple so you will see security warnings on initial installation. We did not want to delay release while waiting for their process to complete.
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Find the most up-to-date information on the [GPT4All Website](https://gpt4all.io/)
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### Raw Model
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[ggml Model Download Link](https://gpt4all.io/models/ggml-gpt4all-j.bin)
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Note this model is only compatible with the C++ bindings found [here](https://github.com/nomic-ai/gpt4all-chat). It will not work with any existing llama.cpp bindings as we had to do a large fork of llama.cpp. GPT4All will support the ecosystem around this new C++ backend going forward.
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Python bindings are imminent and will be integrated into this [repository](https://github.com/nomic-ai/pyllamacpp). Stay tuned on the [GPT4All discord](https://discord.gg/mGZE39AS3e) for updates.
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## Training GPT4All-J
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Please see [GPT4All-J Technical Report](https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf) for details.
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### GPT4All-J Training Data
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- We are releasing the curated training data for anyone to replicate GPT4All-J here: [GPT4All-J Training Data](https://huggingface.co/datasets/nomic-ai/gpt4all-j-prompt-generations)
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- [Atlas Map of Prompts](https://atlas.nomic.ai/map/gpt4all-j-prompts-curated)
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- [Atlas Map of Responses](https://atlas.nomic.ai/map/gpt4all-j-response-curated)
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We have released updated versions of our `GPT4All-J` model and training data.
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- `v1.0`: The original model trained on the v1.0 dataset
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- `v1.1-breezy`: Trained on a filtered dataset where we removed all instances of AI language model
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- `v1.2-jazzy`: Trained on a filtered dataset where we also removed instances like I'm sorry, I can't answer... and AI language model
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The [models](https://huggingface.co/nomic-ai/gpt4all-j) and [data](https://huggingface.co/datasets/nomic-ai/gpt4all-j-prompt-generations) versions can be specified by passing a `revision` argument.
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For example, to load the `v1.2-jazzy` model and dataset, run:
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```python
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM
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dataset = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision="v1.2-jazzy")
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model = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-j-prompt-generations", revision="v1.2-jazzy")
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```
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### GPT4All-J Training Instructions
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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_gptj.json train.py --config configs/train/finetune_gptj.yaml
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```
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# Original GPT4All Model (based on GPL Licensed LLaMa)
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Run on M1 Mac (not sped up!)
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# Try it yourself
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Here's how to get started with the CPU quantized GPT4All model checkpoint:
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1. Download the `gpt4all-lora-quantized.bin` file from [Direct Link](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin) or [[Torrent-Magnet]](https://tinyurl.com/gpt4all-lora-quantized).
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2. Clone this repository, navigate to `chat`, and place the downloaded file there.
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3. Run the appropriate command for your OS:
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- M1 Mac/OSX: `cd chat;./gpt4all-lora-quantized-OSX-m1`
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- Linux: `cd chat;./gpt4all-lora-quantized-linux-x86`
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- Windows (PowerShell): `cd chat;./gpt4all-lora-quantized-win64.exe`
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- Intel Mac/OSX: `cd chat;./gpt4all-lora-quantized-OSX-intel`
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For custom hardware compilation, see our [llama.cpp](https://github.com/zanussbaum/gpt4all.cpp) fork.
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-----------
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Find all compatible models in the GPT4All Ecosystem section.
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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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- M1 Mac/OSX: `cd chat;./gpt4all-lora-quantized-OSX-m1 -m gpt4all-lora-unfiltered-quantized.bin`
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- Linux: `cd chat;./gpt4all-lora-quantized-linux-x86 -m gpt4all-lora-unfiltered-quantized.bin`
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- Windows (PowerShell): `cd chat;./gpt4all-lora-quantized-win64.exe -m gpt4all-lora-unfiltered-quantized.bin`
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- Intel Mac/OSX: `cd chat;./gpt4all-lora-quantized-OSX-intel -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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# Python Client
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## CPU Interface
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To run GPT4All in python, see the new [official Python bindings](https://github.com/nomic-ai/pyllamacpp).
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The old bindings are still available but now deprecated. They will not work in a notebook environment.
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To get running using the python client with the CPU interface, first install the [nomic client](https://github.com/nomic-ai/nomic) using `pip install nomic`
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Then, you can use the following script to interact with GPT4All:
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```
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from nomic.gpt4all import GPT4All
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m = GPT4All()
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m.open()
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m.prompt('write me a story about a lonely computer')
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```
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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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The setup here is slightly more involved than the CPU model.
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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.gpt4all 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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Where LLAMA_PATH is the path to a Huggingface Automodel compliant LLAMA model.
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Nomic is unable to distribute this file at this time.
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We are working on a GPT4All that does not have this limitation right now.
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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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# GPT4All Compatibility Ecosystem
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Edge models in the GPT4All Ecosystem. Please PR as the [community grows](https://huggingface.co/models?sort=modified&search=4bit).
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Feel free to convert this to a more structured table.
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- [gpt4all](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin) [[MD5 Signature](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin.md5)]
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- [gpt4all-ggml-converted](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized-ggml.bin) [[MD5 Signature](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized-ggml.bin.md5)]
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- [gpt4all-unfiltered](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-unfiltered-quantized.bin) [[MD5 Signature](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-unfiltered-quantized.bin.md5)]
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- [ggml-vicuna-7b-4bit](https://huggingface.co/eachadea/ggml-vicuna-7b-4bit)
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- [vicuna-13b-GPTQ-4bit-128g](https://huggingface.co/anon8231489123/vicuna-13b-GPTQ-4bit-128g)
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- [LLaMa-Storytelling-4Bit](https://huggingface.co/GamerUntouch/LLaMa-Storytelling-4Bit)
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- [Alpaca Native 4bit](https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/tree/main)
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# Roadmap
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## Short Term
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- <span style="color:green">(Done)</span> Train a GPT4All model based on GPTJ to alleviate llama distribution issues.
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- <span style="color:green">(Done)</span> Create improved CPU and GPU interfaces for this model.
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- <span style="color:green">(Done)</span> [Integrate llama.cpp bindings](https://github.com/nomic-ai/pyllamacpp)
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- <span style="color:green">(Done)</span> [Create a good conversational chat interface for the model.](https://github.com/nomic-ai/gpt4all-ui)
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- <span style="color:green">(Done)</span> [Allow users to opt in and submit their chats for subsequent training runs](https://github.com/nomic-ai/gpt4all-ui)
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## Medium Term
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- <span style="color:red">(NOT STARTED)</span> Integrate GPT4All with [Atlas](https://atlas.nomic.ai) to allow for document retrieval.
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- BLOCKED by GPT4All based on GPTJ
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- <span style="color:red">(Done)</span> Integrate GPT4All with Langchain.
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- <span style="color:green">(IN PROGRESS)</span> Build easy custom training scripts to allow users to fine tune models.
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## Long Term
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- <span style="color:red">(NOT STARTED)</span> Allow anyone to curate training data for subsequent GPT4All releases using Atlas.
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- <span style="color:green">(IN PROGRESS)</span> Democratize AI.
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# Reproducibility
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Trained Model 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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- gpt4all-j (one full epoch of training) (https://huggingface.co/nomic-ai/gpt4all-j)
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- gpt4all-j-lora (one full epoch of training) (https://huggingface.co/nomic-ai/gpt4all-j-lora)
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Raw Data:
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- [Training Data Without P3](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations)
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- Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean_without_p3
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- [Full Dataset with P3](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations_with_p3)
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- Explorer: https://atlas.nomic.ai/map/gpt4all_data_clean
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- [GPT4All-J Dataset](https://huggingface.co/datasets/nomic-ai/gpt4all-j-prompt-generations)
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- Explorer Indexed on Prompts: https://atlas.nomic.ai/map/gpt4all-j-prompts-curated
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- Exporer Indexed on Responses: https://atlas.nomic.ai/map/gpt4all-j-response-curated
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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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```
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git clone --recurse-submodules https://github.com/nomic-ai/gpt4all.git
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git submodule update --init
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```
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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 ../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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## Need Help?
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Join the <a href="https://discord.gg/E7q6fp9bmx"> Discord </a> and ask for help in `#gpt4all-help`
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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
|
||||||
|
|
||||||
|
>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 Caesar into a caesar salad in iambic pentameter.
|
||||||
|
|
||||||
|
> The fall of Julius Caesar into a caesar salad<br>
|
||||||
|
> In iambic pentameter,<br>
|
||||||
|
> The mighty Roman emperor<br>
|
||||||
|
> Fell into a salad of lettuce and croutons,<br>
|
||||||
|
> His empire crumbling around him,<br>
|
||||||
|
> As he was devoured by the hungry diners.<br>
|
||||||
|
> The once mighty emperor<br>
|
||||||
|
> Was now just a salad topping,<br>
|
||||||
|
> His legacy reduced to a mere garnish,<br>
|
||||||
|
> As he was consumed by the masses.<br>
|
||||||
|
> The fall of Julius Caesar into a caesar salad,<br>
|
||||||
|
> In iambic pentameter,<br>
|
||||||
|
> Was a tragic end to a great man,<br>
|
||||||
|
> Who had once ruled the world with an iron fist.<br>
|
||||||
|
> But now, he was just a salad topping,<br>
|
||||||
|
> His legacy reduced to a mere garnish,<br>
|
||||||
|
> As he was consumed by the masses.
|
||||||
|
|
||||||
|
### What is a three word topic describing the following keywords: baseball, football, soccer:
|
||||||
|
> Sports, athletics, games
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
If you utilize this repository, 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}},
|
||||||
|
}
|
||||||
|
```
|
||||||
Loading…
Reference in New Issue
Block a user