mirror of
https://github.com/nomic-ai/gpt4all.git
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Merge: main into gptj
This commit is contained in:
parent
8a94a8c068
commit
a3485c4b32
7
.gitignore
vendored
7
.gitignore
vendored
@ -164,4 +164,9 @@ cython_debug/
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
|
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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||||
#.idea/
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||||
#.idea/
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||||
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||||
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||||
# vs code
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.vscode
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*.bin
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19
LICENSE.txt
Normal file
19
LICENSE.txt
Normal file
@ -0,0 +1,19 @@
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||||
Copyright (c) 2023 Nomic, Inc.
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||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
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202
README.md
202
README.md
@ -1,13 +1,33 @@
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<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">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">
|
||||
<a href="https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf">:green_book: Technical Report</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://discord.gg/kvmy6dQB">Discord</a>
|
||||
<a href="https://github.com/nomic-ai/pyllamacpp">:snake: Official Python Bindings</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/nomic-ai/gpt4all-ts">:computer: Official Typescript Bindings</a>
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://github.com/nomic-ai/gpt4all-ui">:speech_balloon: Official Chat Interface</a>
|
||||
</p>
|
||||
|
||||
<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>
|
||||
</p>
|
||||
|
||||
|
||||
<p align="center">
|
||||
<a href="https://discord.gg/mGZE39AS3e">Discord</a>
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</p>
|
||||
|
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|
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|
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![gpt4all-lora-demo](https://user-images.githubusercontent.com/13879686/228352356-de66ca7a-df70-474e-b929-2e3656165051.gif)
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@ -16,20 +36,99 @@ 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).
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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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Clone this repository down and place the quantized model in the `chat` directory and start chatting by running:
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For custom hardware compilation, see our [llama.cpp](https://github.com/zanussbaum/gpt4all.cpp) fork.
|
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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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-----------
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Find all compatible models in the GPT4All Ecosystem section.
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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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[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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|
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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`
|
||||
- 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.
|
||||
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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```
|
||||
Where LLAMA_PATH is the path to a Huggingface Automodel compliant LLAMA model.
|
||||
Nomic is unable to distribute this file at this time.
|
||||
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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|
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# GPT4All Compatibility Ecosystem
|
||||
Edge models in the GPT4All Ecosystem. Please PR as the [community grows](https://huggingface.co/models?sort=modified&search=4bit).
|
||||
Feel free to convert this to a more structured table.
|
||||
|
||||
- [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)]
|
||||
- [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)]
|
||||
- [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
|
||||
- <span style="color:green">(IN PROGRESS)</span> Train a GPT4All model based on GPTJ to alleviate llama distribution issues.
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- <span style="color:green">(IN PROGRESS)</span> Create improved CPU and GPU interfaces for this model.
|
||||
- <span style="color:green">(Done)</span> [Integrate llama.cpp bindings](https://github.com/nomic-ai/pyllamacpp)
|
||||
- <span style="color:green">(Done)</span> [Create a good conversational chat interface for the model.](https://github.com/nomic-ai/gpt4all-ui)
|
||||
- <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
|
||||
- <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
|
||||
- <span style="color:red">(NOT STARTED)</span> Integrate GPT4All with Langchain.
|
||||
- <span style="color:green">(IN PROGRESS)</span> Build easy custom training scripts to allow users to fine tune models.
|
||||
|
||||
## Long Term
|
||||
- <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 LoRa Weights:
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||||
@ -37,9 +136,9 @@ Trained LoRa Weights:
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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:
|
||||
- [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)
|
||||
- [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
|
||||
- [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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- [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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|
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We are not distributing a LLaMa 7B checkpoint.
|
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@ -50,9 +149,10 @@ You can reproduce our trained model by doing the following:
|
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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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```
|
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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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@ -78,6 +178,10 @@ accelerate launch --dynamo_backend=inductor --num_processes=8 --num_machines=1 -
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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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## Need Help?
|
||||
|
||||
Join the <a href="https://discord.gg/kvmy6dQB"> Discord </a> and ask for help in `#gpt4all-help`
|
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|
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# Sample Generations
|
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|
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### Provide instructions for the given exercise. Leg Raises
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@ -104,7 +208,8 @@ python generate.py --config configs/generate/generate.yaml --prompt "Write a scr
|
||||
|
||||
### 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.
|
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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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|
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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:
|
||||
@ -114,42 +219,43 @@ python generate.py --config configs/generate/generate.yaml --prompt "Write a scr
|
||||
> print(reversed_str) # Output will be reversed version of the inputted string.
|
||||
|
||||
### 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
|
||||
>10. Pug
|
||||
> 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
|
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>In iambic pentameter,
|
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>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.
|
||||
### 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
|
||||
> Sports, athletics, games
|
||||
|
||||
## Citation
|
||||
|
||||
|
||||
If you utilize this reposistory, models or data in a downstream project, please consider citing it with:
|
||||
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},
|
||||
@ -160,7 +266,3 @@ If you utilize this reposistory, models or data in a downstream project, please
|
||||
howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
|
||||
}
|
||||
```
|
||||
|
||||
### Alternative Download Locations
|
||||
#### gpt4all-lora-quantized.bin Backup Torrent Link
|
||||
magnet:?xt=urn:btih:1F11A9691EE06C18F0040E359361DCA0479BCB5A&dn=gpt4all-lora-quantized.bin&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%2Fopentracker.i2p.rocks%3A6969%2Fannounce
|
||||
|
@ -160,7 +160,7 @@ We realized that we had two bugs however:
|
||||
- We accidentally duplicated data and effectively trained for 2 epochs instead of 1
|
||||
- We added an eos token to every sequence, even those that we truncated (e.g. long code that exceeds the 1024).
|
||||
|
||||
## Conditonal EOS and 1 Epoch
|
||||
## Conditional EOS and 1 Epoch
|
||||
|
||||
Using the same parameters, we then trained a model using a "conditional" eos token where we only add an `eos` when the inputs are less than the maximum sequence length for one epoch.
|
||||
|
||||
|
3
data.py
3
data.py
@ -62,7 +62,6 @@ def load_data(config, tokenizer):
|
||||
dataset_path = config["dataset_path"]
|
||||
|
||||
if os.path.exists(dataset_path):
|
||||
# check if path is a directory
|
||||
if os.path.isdir(dataset_path):
|
||||
files = glob.glob(os.path.join(dataset_path, "*_clean.jsonl"))
|
||||
else:
|
||||
@ -92,7 +91,7 @@ def load_data(config, tokenizer):
|
||||
**kwargs
|
||||
)
|
||||
val_dataset = val_dataset.map(
|
||||
lambda ele: tokenize_inputs(config, tokenizer, ele),
|
||||
lambda ele: tokenize_inputs(config, tokenizer, ele),
|
||||
batched=True,
|
||||
remove_columns=["source", "prompt"],
|
||||
**kwargs
|
||||
|
88
launcher.sh
Normal file
88
launcher.sh
Normal file
@ -0,0 +1,88 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Display header
|
||||
echo "=========================================================="
|
||||
echo " ██████ ██████ ████████ ██ ██ █████ ██ ██ "
|
||||
echo "██ ██ ██ ██ ██ ██ ██ ██ ██ ██ "
|
||||
echo "██ ███ ██████ ██ ███████ ███████ ██ ██ "
|
||||
echo "██ ██ ██ ██ ██ ██ ██ ██ ██ "
|
||||
echo " ██████ ██ ██ ██ ██ ██ ███████ ███████ "
|
||||
echo " └─> https://github.com/nomic-ai/gpt4all"
|
||||
|
||||
# Function to detect macOS architecture and set the binary filename
|
||||
detect_mac_arch() {
|
||||
local mac_arch
|
||||
mac_arch=$(uname -m)
|
||||
case "$mac_arch" in
|
||||
arm64)
|
||||
os_type="M1 Mac/OSX"
|
||||
binary_filename="gpt4all-lora-quantized-OSX-m1"
|
||||
;;
|
||||
x86_64)
|
||||
os_type="Intel Mac/OSX"
|
||||
binary_filename="gpt4all-lora-quantized-OSX-intel"
|
||||
;;
|
||||
*)
|
||||
echo "Unknown macOS architecture"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
}
|
||||
|
||||
# Detect operating system and set the binary filename
|
||||
case "$(uname -s)" in
|
||||
Darwin*)
|
||||
detect_mac_arch
|
||||
;;
|
||||
Linux*)
|
||||
if grep -q Microsoft /proc/version; then
|
||||
os_type="Windows (WSL)"
|
||||
binary_filename="gpt4all-lora-quantized-win64.exe"
|
||||
else
|
||||
os_type="Linux"
|
||||
binary_filename="gpt4all-lora-quantized-linux-x86"
|
||||
fi
|
||||
;;
|
||||
CYGWIN*|MINGW32*|MSYS*|MINGW*)
|
||||
os_type="Windows (Cygwin/MSYS/MINGW)"
|
||||
binary_filename="gpt4all-lora-quantized-win64.exe"
|
||||
;;
|
||||
*)
|
||||
echo "Unknown operating system"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
echo "================================"
|
||||
echo "== You are using $os_type."
|
||||
|
||||
|
||||
# Change to the chat directory
|
||||
cd chat
|
||||
|
||||
# List .bin files and prompt user to select one
|
||||
bin_files=(*.bin)
|
||||
echo "== Available .bin files:"
|
||||
for i in "${!bin_files[@]}"; do
|
||||
echo " [$((i+1))] ${bin_files[i]}"
|
||||
done
|
||||
|
||||
# Function to get user input and validate it
|
||||
get_valid_user_input() {
|
||||
local input_valid=false
|
||||
|
||||
while ! $input_valid; do
|
||||
echo "==> Please enter a number:"
|
||||
read -r user_selection
|
||||
if [[ $user_selection =~ ^[0-9]+$ ]] && (( user_selection >= 1 && user_selection <= ${#bin_files[@]} )); then
|
||||
input_valid=true
|
||||
else
|
||||
echo "Invalid input. Please enter a number between 1 and ${#bin_files[@]}."
|
||||
fi
|
||||
done
|
||||
}
|
||||
|
||||
get_valid_user_input
|
||||
selected_bin_file="${bin_files[$((user_selection-1))]}"
|
||||
|
||||
# Run the selected .bin file with the appropriate command
|
||||
./"$binary_filename" -m "$selected_bin_file"
|
Loading…
Reference in New Issue
Block a user