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README.md
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<p align="center">GPT4All runs large language models (LLMs) privately on everyday desktops & laptops. <br> <br> No API calls or GPUs required - you can just download the application and <a href="https://docs.gpt4all.io/gpt4all_desktop/quickstart.html#quickstart">get started</a> <p align="center">GPT4All runs large language models (LLMs) privately on everyday desktops & laptops. <br> <br> No API calls or GPUs required - you can just download the application and <a href="https://docs.gpt4all.io/gpt4all_desktop/quickstart.html#quickstart">get started</a>
https://github.com/nomic-ai/gpt4all/assets/70534565/513a0f15-4964-4109-89e4-4f9a9011f311
<p align="center"> <p align="center">
<a href="https://gpt4all.io/installers/gpt4all-installer-win64.exe"> <a href="https://gpt4all.io/installers/gpt4all-installer-win64.exe">
@ -12,15 +13,15 @@
<p align="center"> <p align="center">
<a href="https://gpt4all.io/installers/gpt4all-installer-darwin.dmg"> <a href="https://gpt4all.io/installers/gpt4all-installer-darwin.dmg">
<img src="gpt4all-bindings/python/docs/assets/mac.png" width="80" height="90"><br> <img src="gpt4all-bindings/python/docs/assets/mac.png" width="85" height="100"><br>
Download for MacOS Download for MacOS
</a> </a>
</p> </p>
<p align="center"> <p align="center">
<a href="https://gpt4all.io/installers/gpt4all-installer-linux.run"> <a href="https://gpt4all.io/installers/gpt4all-installer-linux.run">
<img src="gpt4all-bindings/python/docs/assets/linux.png" width="80" height="80"><br> <img src="gpt4all-bindings/python/docs/assets/ubuntu.svg" width="120" height="120"><br>
Download for Linux Download for Ubuntu
</a> </a>
</p> </p>
@ -37,8 +38,6 @@ GPT4All is made possible by our compute partner <a href="https://www.paperspace.
<a href="https://www.phorm.ai/query?projectId=755eecd3-24ad-49cc-abf4-0ab84caacf63"><img src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg" alt="phorm.ai"></a> <a href="https://www.phorm.ai/query?projectId=755eecd3-24ad-49cc-abf4-0ab84caacf63"><img src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg" alt="phorm.ai"></a>
</p> </p>
## Install GPT4All Python ## Install GPT4All Python
`gpt4all` gives you access to LLMs with our Python client around [`llama.cpp`](https://github.com/ggerganov/llama.cpp) implementations. `gpt4all` gives you access to LLMs with our Python client around [`llama.cpp`](https://github.com/ggerganov/llama.cpp) implementations.
@ -57,10 +56,17 @@ with model.chat_session():
``` ```
### Release History ## Integrations
:parrot::link: [Langchain](https://python.langchain.com/v0.2/docs/integrations/providers/gpt4all/)
:card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all)
:telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all)
## Release History
- **July 2nd, 2024**: V3.0.0 Release - **July 2nd, 2024**: V3.0.0 Release
- New UI/UX: fresh redesign of the chat application GUI and user experience - Fresh redesign of the chat application UI
- LocalDocs: bring information from files on-device into chats - Improved user workflow for LocalDocs
- Expanded access to more model architectures
- **October 19th, 2023**: GGUF Support Launches with Support for: - **October 19th, 2023**: GGUF Support Launches with Support for:
- Mistral 7b base model, an updated model gallery on [gpt4all.io](https://gpt4all.io), several new local code models including Rift Coder v1.5 - Mistral 7b base model, an updated model gallery on [gpt4all.io](https://gpt4all.io), several new local code models including Rift Coder v1.5
- [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan) support for Q4\_0 and Q4\_1 quantizations in GGUF. - [Nomic Vulkan](https://blog.nomic.ai/posts/gpt4all-gpu-inference-with-vulkan) support for Q4\_0 and Q4\_1 quantizations in GGUF.
@ -71,13 +77,6 @@ with model.chat_session():
[Docker-based API server]: https://github.com/nomic-ai/gpt4all/tree/cef74c2be20f5b697055d5b8b506861c7b997fab/gpt4all-api [Docker-based API server]: https://github.com/nomic-ai/gpt4all/tree/cef74c2be20f5b697055d5b8b506861c7b997fab/gpt4all-api
### Integrations
* :parrot::link: [Langchain](https://python.langchain.com/v0.2/docs/integrations/providers/gpt4all/)
* :card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all)
* :telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all)
## Contributing ## Contributing
GPT4All welcomes contributions, involvement, and discussion from the open source community! GPT4All welcomes contributions, involvement, and discussion from the open source community!
Please see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates. Please see CONTRIBUTING.md and follow the issues, bug reports, and PR markdown templates.
@ -86,22 +85,6 @@ Check project discord, with project owners, or through existing issues/PRs to av
Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost. Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost.
Example tags: `backend`, `bindings`, `python-bindings`, `documentation`, etc. Example tags: `backend`, `bindings`, `python-bindings`, `documentation`, etc.
## Technical Reports
<p align="center">
<a href="https://gpt4all.io/reports/GPT4All_Technical_Report_3.pdf">:green_book: Technical Report 3: GPT4All Snoozy and Groovy </a>
</p>
<p align="center">
<a href="https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf">:green_book: Technical Report 2: GPT4All-J </a>
</p>
<p align="center">
<a href="https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf">:green_book: Technical Report 1: GPT4All</a>
</p>
## Citation ## Citation
If you utilize this repository, 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:

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gpt4all-bindings/python/docs/gpt4all_desktop/models.md
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@ -56,13 +56,13 @@ Many LLMs are available at various sizes, quantizations, and licenses.
Here are a few examples: Here are a few examples:
| Model| Filesize| RAM Required| Parameters| Developer| License| MD5 Sum (Unique Hash)| | Model| Filesize| RAM Required| Parameters| Quantization| Developer| License| MD5 Sum (Unique Hash)|
|------|---------|-------------|-----------|----------|--------|----------------------| |------|---------|-------------|-----------|-------------|----------|--------|----------------------|
| Llama 3 Instruct | 4.66 GB| 8 GB| 8 Billion| Meta| [Llama 3 License](https://llama.meta.com/llama3/license/)| c87ad09e1e4c8f9c35a5fcef52b6f1c9| | Llama 3 Instruct | 4.66 GB| 8 GB| 8 Billion| q4_0| Meta| [Llama 3 License](https://llama.meta.com/llama3/license/)| c87ad09e1e4c8f9c35a5fcef52b6f1c9|
| Nous Hermes 2 Mistral DPO| 4.21 GB| 8 GB| 7 Billion| Mistral & Nous Research | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)| Coa5f6b4eabd3992da4d7fb7f020f921eb| | Nous Hermes 2 Mistral DPO| 4.11 GB| 8 GB| 7 Billion| q4_0| Mistral & Nous Research | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)| Coa5f6b4eabd3992da4d7fb7f020f921eb|
| Phi-3 Mini Instruct | 2.03 GB| 4 GB| 4 billion| Microsoft| [MIT](https://opensource.org/license/mit)| f8347badde9bfc2efbe89124d78ddaf5| | Phi-3 Mini Instruct | 2.18 GB| 4 GB| 4 billion| q4_0| Microsoft| [MIT](https://opensource.org/license/mit)| f8347badde9bfc2efbe89124d78ddaf5|
| Mini Orca (Small)| 1.84 GB| 4 GB| 3 billion| Microsoft | [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0)| 0e769317b90ac30d6e09486d61fefa26| | Mini Orca (Small)| 1.98 GB| 4 GB| 3 billion| q4_0| Microsoft | [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0)| 0e769317b90ac30d6e09486d61fefa26|
| GPT4All Snoozy| 7.36 GB| 16 GB| 13 billion| Nomic AI| [GPL](https://www.gnu.org/licenses/gpl-3.0.en.html)| 40388eb2f8d16bb5d08c96fdfaac6b2c| | GPT4All Snoozy| 7.37 GB| 16 GB| 13 billion| q4_0| Nomic AI| [GPL](https://www.gnu.org/licenses/gpl-3.0.en.html)| 40388eb2f8d16bb5d08c96fdfaac6b2c|
### Search Results ### Search Results

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gpt4all-bindings/python/docs/gpt4all_help/faq.md
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### Which language models are supported? ### Which language models are supported?
Our backend supports models with a `llama.cpp` implementation which have been uploaded to [HuggingFace](https://huggingface.co/). We support models with a `llama.cpp` implementation which have been uploaded to [HuggingFace](https://huggingface.co/).
### Which embedding models are supported? ### Which embedding models are supported?
The following embedding models can be used within the application and with the `Embed4All` class from the `gpt4all` Python library. The default context length as GGUF files is 2048 but can be [extended](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF#description). We support SBert and Nomic Embed Text v1 & v1.5.
| Name | Initializing with `Embed4All` | Context Length | Embedding Length | File Size |
|--------------------|------------------------------------------------------|---------------:|-----------------:|----------:|
| [SBert](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)| ```pythonemb = Embed4All("all-MiniLM-L6-v2.gguf2.f16.gguf")```| 512 | 384 | 44 MiB |
| [Nomic Embed v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF) | nomic&#x2011;embed&#x2011;text&#x2011;v1.f16.gguf| 2048 | 768 | 262 MiB |
| [Nomic Embed v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF) | nomic&#x2011;embed&#x2011;text&#x2011;v1.5.f16.gguf| 2048 | 64-768 | 262 MiB |
## Software ## Software

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gpt4all-bindings/python/docs/gpt4all_python/home.md
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@ -23,6 +23,15 @@ Models are loaded by name via the `GPT4All` class. If it's your first time loadi
print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024)) print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))
``` ```
| `GPT4All` model name| Filesize| RAM Required| Parameters| Quantization| Developer| License| MD5 Sum (Unique Hash)|
|------|---------|-------|-------|-----------|----------|--------|----------------------|
| `Meta-Llama-3-8B-Instruct.Q4_0.gguf`| 4.66 GB| 8 GB| 8 Billion| q4_0| Meta| [Llama 3 License](https://llama.meta.com/llama3/license/)| c87ad09e1e4c8f9c35a5fcef52b6f1c9|
| `Nous-Hermes-2-Mistral-7B-DPO.Q4_0.gguf`| 4.11 GB| 8 GB| 7 Billion| q4_0| Mistral & Nous Research | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)| Coa5f6b4eabd3992da4d7fb7f020f921eb|
| `Phi-3-mini-4k-instruct.Q4_0.gguf` | 2.18 GB| 4 GB| 3.8 billion| q4_0| Microsoft| [MIT](https://opensource.org/license/mit)| f8347badde9bfc2efbe89124d78ddaf5|
| `orca-mini-3b-gguf2-q4_0.gguf`| 1.98 GB| 4 GB| 3 billion| q4_0| Microsoft | [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0)| 0e769317b90ac30d6e09486d61fefa26|
| `gpt4all-13b-snoozy-q4_0.gguf`| 7.37 GB| 16 GB| 13 billion| q4_0| Nomic AI| [GPL](https://www.gnu.org/licenses/gpl-3.0.en.html)| 40388eb2f8d16bb5d08c96fdfaac6b2c|
## Chat Session Generation ## Chat Session Generation
Most of the language models you will be able to access from HuggingFace have been trained as assistants. This guides language models to not just answer with relevant text, but *helpful* text. Most of the language models you will be able to access from HuggingFace have been trained as assistants. This guides language models to not just answer with relevant text, but *helpful* text.
@ -75,16 +84,6 @@ If you want your LLM's responses to be helpful in the typical sense, we recommen
b = 5 b = 5
``` ```
## Example Models
| Model| Filesize| RAM Required| Parameters| Developer| License| MD5 Sum (Unique Hash)|
|------|---------|-------------|-----------|----------|--------|----------------------|
| `Meta-Llama-3-8B-Instruct.Q4_0.gguf` | 4.66 GB| 8 GB| 8 Billion| Meta| [Llama 3 License](https://llama.meta.com/llama3/license/)| c87ad09e1e4c8f9c35a5fcef52b6f1c9|
| Nous Hermes 2 Mistral DPO| 4.21 GB| 8 GB| 7 Billion| Mistral & Nous Research | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)| Coa5f6b4eabd3992da4d7fb7f020f921eb|
| Phi-3 Mini Instruct | 2.03 GB| 4 GB| 4 billion| Microsoft| [MIT](https://opensource.org/license/mit)| f8347badde9bfc2efbe89124d78ddaf5|
| Mini Orca (Small)| 1.84 GB| 4 GB| 3 billion| Microsoft | [CC-BY-NC-SA-4.0](https://spdx.org/licenses/CC-BY-NC-SA-4.0)| 0e769317b90ac30d6e09486d61fefa26|
| GPT4All Snoozy| 7.36 GB| 16 GB| 13 billion| Nomic AI| [GPL](https://www.gnu.org/licenses/gpl-3.0.en.html)| 40388eb2f8d16bb5d08c96fdfaac6b2c|
## Direct Generation ## Direct Generation
Directly calling `model.generate()` prompts the model without applying any templates. Directly calling `model.generate()` prompts the model without applying any templates.
@ -150,3 +149,11 @@ The easiest way to run the text embedding model locally uses the [`nomic`](https
![Nomic embed text local inference](../assets/local_embed.gif) ![Nomic embed text local inference](../assets/local_embed.gif)
To learn more about making embeddings locally with `nomic`, visit our [embeddings guide](https://docs.nomic.ai/atlas/guides/embeddings#local-inference). To learn more about making embeddings locally with `nomic`, visit our [embeddings guide](https://docs.nomic.ai/atlas/guides/embeddings#local-inference).
The following embedding models can be used within the application and with the `Embed4All` class from the `gpt4all` Python library. The default context length as GGUF files is 2048 but can be [extended](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF#description).
| Name| Using with `nomic`| `Embed4All` model name| Context Length| # Embedding Dimensions| File Size|
|--------------------|-|------------------------------------------------------|---------------:|-----------------:|----------:|
| [Nomic Embed v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1-GGUF) | ```embed.text(strings, model="nomic-embed-text-v1", inference_mode="local")```| ```Embed4All("nomic-embed-text-v1.f16.gguf")```| 2048 | 768 | 262 MiB |
| [Nomic Embed v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF) | ```embed.text(strings, model="nomic-embed-text-v1.5", inference_mode="local")```| ```Embed4All("nomic-embed-text-v1.5.f16.gguf")``` | 2048| 64-768 | 262 MiB |
| [SBert](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)| n/a| ```Embed4All("all-MiniLM-L6-v2.gguf2.f16.gguf")```| 512 | 384 | 44 MiB |

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gpt4all-training/README.md
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## Training GPT4All-J ## Training GPT4All-J
Please see [GPT4All-J Technical Report](https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf) for details. ### Technical Reports
<p align="center">
<a href="https://gpt4all.io/reports/GPT4All_Technical_Report_3.pdf">:green_book: Technical Report 3: GPT4All Snoozy and Groovy </a>
</p>
<p align="center">
<a href="https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf">:green_book: Technical Report 2: GPT4All-J </a>
</p>
<p align="center">
<a href="https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf">:green_book: Technical Report 1: GPT4All</a>
</p>
### GPT4All-J Training Data ### GPT4All-J Training Data

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roadmap.md
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@ -11,15 +11,15 @@ Each item should have an issue link below.
- [ ] Portuguese - [ ] Portuguese
- [ ] Your native language here. - [ ] Your native language here.
- UI Redesign: an internal effort at Nomic to improve the UI/UX of gpt4all for all users. - UI Redesign: an internal effort at Nomic to improve the UI/UX of gpt4all for all users.
- [ ] Design new user interface and gather community feedback - [x] Design new user interface and gather community feedback
- [ ] Implement the new user interface and experience. - [x] Implement the new user interface and experience.
- Installer and Update Improvements - Installer and Update Improvements
- [ ] Seamless native installation and update process on OSX - [ ] Seamless native installation and update process on OSX
- [ ] Seamless native installation and update process on Windows - [ ] Seamless native installation and update process on Windows
- [ ] Seamless native installation and update process on Linux - [ ] Seamless native installation and update process on Linux
- Model discoverability improvements: - Model discoverability improvements:
- [x] Support huggingface model discoverability - [x] Support huggingface model discoverability
- [ ] Support Nomic hosted model discoverability - [x] Support Nomic hosted model discoverability
- LocalDocs (towards a local perplexity) - LocalDocs (towards a local perplexity)
- Multilingual LocalDocs Support - Multilingual LocalDocs Support
- [ ] Create a multilingual experience - [ ] Create a multilingual experience

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