Announcing Nomic Embed v1.5 🪆🪆🪆
- Variable sized embeddings with matryoshka learning and an 8192 context.
- Outperforms OpenAI text-embedding-3-small across output sizes.
- Open source, open training code, open data.
Day 0 in
@LangChainAI
,
@llama_index
and
@MongoDB
Introducing Nomic Embed - the first fully open long context text embedder to beat OpenAI
- Open source, open weights, open data
- Beats OpenAI text-embeding-3-small and Ada on short and long context benchmarks
- Day 1 integrations with
@langchain
,
@llama
-index,
@MongoDB
Local LLMs now have plugins! 💥
GPT4All LocalDocs allows you chat with your private data!
- Drag and drop files into a directory that GPT4All will query for context when answering questions.
- Supports 40+ filetypes
- Cites sources.
GPT4All now supports Text Embeddings ⚡
- Generate text embeddings of arbitrary length documents for free on CPU at 8,000 tok/second.
- No external dependencies except C.
Big New Release of GPT4All📶
You can now use local CPU-powered LLMs through a familiar API!
Building with a local LLM is as easy as a 1 line code change! Simply spin up the chat app at and place it in server mode!
Documentation:
GPT4All now supports 100+ more models!💥
Nearly every custom ggML model you find
@huggingface
for CPU inference will *just work* with all GPT4All software with the newest release!
Instructions:
Huge Release of GPT4All 💥
Powerful LLM's just got faster!
- Anyone can try
@MosaicML
's new MPT model on their desktop! No GPU required!
- Runs on Windows/Mac/Ubuntu
Try it at:
Private and Local Chat with Your Data is here!
Use any Local LLM with GPT4All LocalDocs to chat with your large collections of PDFs, docx files including financial documents!
- No internet required
- Supports 40 filetypes!
- CPU and GPU
Try LocalDocs in
Monthly reminder for everyone affected by today's
@OpenAI
outage: Local
#GPT4All
models like
@MistralAI
7B run at 20tokens/sec+ on a Macbook air and don't go down.
The first GPT4All powered code copilot has launched🖥️
@morph_labs
allows you to use the recently released Replit GPT4All model on Apple Metal to perform privacy aware
- Code completion (23 tok/second)
- Chatting and asking questions
all through the Rift VSCode extension.
Local…
The future of AI code assistants is open-source, private, secure, and on-device. That future starts today. We’re excited to release Rift, an open-source AI-native language server and VSCode extension for local copilots.
We’ve just raised a $17m Series A round led by
@Coatue
to build explainable and accessible AI.
Join us:
Here is what this means for the future of AI: 🧵
Local LLMs in GPT4All are now 2x faster on Apple Silicone ⚡
- Supports all LLaMa models
- Exclusive support of the Replit model for 23 tok/s code generation enabling local Copilot!
Watch the 13B parameter Hermes model run at 15 tok/s locally!
Releasing GPT4All v2.5.0 with GGUF Support
- Runs
@MistralAI
7B Locally with Vulkan GPU Support
- Universal GPU Inference: Mistral, LLaMa, MPT, Falcon in Chat Client and Python
- Generate Embed4All Embeddings on GPU.
See release notes at
Atlas Capability Announcement: Scalable Duplicate Detection 🍡
- Deduplicate your text, image and embedding datasets in your web browser.
- Scales to millions of datapoints (e.g. English Wikipedia)
- Cross correlate with real-time regex search and semantic lasso's.
First, we collected a training dataset of 1 million prompt-response pairs from GPT-3.5-Turbo on a variety of topics. We are publicly releasing all of this data alongside GPT4All.
Run LLMs on Any GPU with GPT4All ⚡
- Supports all modern
@AMD
,
@intel
,
@Qualcomm
and
@nvidia
GPUs for quantized LLM inference.
- Faster than OpenCL on modern Nvidia GPUs
- Works out of the box on Windows, OSX and Linux.
Details below.
Open source models are not replicable unless you have access to their training data.
We release our training dataset of 235M curated text pairs to enable anyone to replicate Nomic Embed from scratch.
Blog:
btw - you can just do >0 on the nomic-embed-text-v1.5 outputs to get binary embeddings that maintain 90%+ of the FP32 MTEB performance ;)
get the model on
@huggingface
:
🚀 𝐂𝐨𝐡𝐞𝐫𝐞 𝐄𝐦𝐛𝐞𝐝 𝐕𝟑 - 𝐢𝐧𝐭𝟖 & 𝐛𝐢𝐧𝐚𝐫𝐲 𝐒𝐮𝐩𝐩𝐨𝐫𝐭🚀
I'm excited to launch our native support for int8 & binary embeddings for Cohere Embed V3.
They slash your vector DB cost 4x - 32x while keeping 95% - 100% of the search quality.
New Release GPT4All: v2.7.0
This version has support for a wide range of new model architectures as well as many bug fixes.
- Baichuan, BLOOM, CodeShell, GPT-2, Orion, Persimmon, Phi and Phi-2, Plamo, Qwen, Qwen2, Refact, and StableLM
Next, we used Atlas to curate the data. We removed low diversity responses, and ensured that the training data covered a variety of topics. Explore the full train set on Atlas:
What can you learn about a multimodal LLM from its training data?
Read about how
@huggingface
evaluated and improved their multimodal LLM IDEFICS with Nomic Atlas
We then benched our trained model against the best open source alpaca-lora we could find on
@huggingface
(tloen/alpaca-lora-7b by
@ecjwg
). Our model achieves consistently lower perplexity!
Nomic is proud to support efforts democratizing access to large language models. We believe that open source models are critical to advancing AI research, particularly in the fields of AI interpretability and alignment.
One of our discord community members benched
#GPT4All
on trivia, and it ended up beating all other models, including GPT-3.5!
Check out the evaluation here:
Rather large 🦜🔗0.0.131 release!
🆓GPT4all model (
@nomic_ai
)
🦙Llama-cpp model
⏹️Support for
@qdrant_engine
local db
🌲Zilliz cloud (
@milvusio
) Vectorstore support
📧New OutlookMessage Document Loader
🕸️New Selenium Document Loader
🪟 Support for SQL views in SQLChain
🧵
LocalDocs enables any GPT4All model to cite its sources.
When GPT4All decides that it can improve response factuality by using your documents it does so and tells you which documents it used.
Announcing GPT4All-J: The First Apache-2 Licensed Chatbot That Runs Locally on Your Machine💥
Large Language Models must be democratized and decentralized.
Embedding evaluation is broken. Benchmarks like MTEB are not sufficient for capturing all aspects of model behavior.
You can discover systematic differences in model embedding spaces using Nomic Atlas
Comparing nomic-embed-text-v1 and OpenAI Ada 002 embeddings.…
We are proud to announce today that we are partnering with
@HuggingFace
, the north star of open source machine learning. Together, we are creating and distributing rich, interactive data visualizations to help everyone understand the data going into their AI systems.
We're excited to announce that the Nomic Vulkan backend is now merged into
@ggerganov
's llama.cpp under an MIT license!
Run open-source LLMs on nearly any GPU.
How do you get 10 million documents of text ready for generative AI model training?
Join the first episode of the Nomic Atlas Webinar Series to learn!
January 19th, 12PM EST
@gdb
Traditional methods for manual inspection of unstructured data are tedious - a web interface that shows you all of your data pre-organized makes it easy. This is exactly why we are focused on scaling Atlas to support internet scale datasets. Better data curation = better ML.
Under the hood, GPT4All now supports the contrastively trained models for inference.
This enables anyone to deploy powerful text embeddings models at no cost.
Install the universal local LLM client from , go to settings and enable the plugin!
Documentation:
You will soon be able to use LocalDocs in server mode allowing you to easily augment any LLM with your private data.
Interested in learning more about Nomic Embed, the first truly open source model to outperform OpenAI?
Nomic sat down with
@mattturck
on
@FirstMarkCap
's MAD podcast to talk all things Nomic, Atlas, and Embed.
We also launch the Nomic Embedding API
- 1M Free tokens!
- Production ready embedding inference API including task specific embedding customizations.
- Deep integration with Atlas Datasets
- New models incoming 👀
Sign up at
@krea_ai
How it works 👇
Every point is a user-generated image and its prompt.
Points are close together if an AI considers their images similar.
For example, Billionaires Row is a region containing co-located generations of
@elonmusk
, Jeff Bezos, Mark Zuckerburg and US dollars.
Excited to see
#gpt4all
as the top project on this list! Thrilled to keep building this community with awesome open source companies like
@huggingface
. Congrats on the milestone!
🤗 Transformers has been built by, with, and for the community.
Reaching 100k ⭐ on GitHub is a testament to ML's reach and the community's will to innovate and contribute.
To celebrate, we highlight 100 incredible projects in transformers' vicinity.
To make this possible, GPT4All hackers had to implement several custom Apple Metal kernels for LLM ops (e.g. Alibi) and support a custom fork llama.cpp!
Excited to get these changes upstream!
GPT4All will auto-index your arbitrary corpus of documents with a simple desktop GUI allowing you to securely and privately chat with your data.
Switch between any local LLM including
@MistralAI
!
Learn more in the documentation:
We have first Falcon 40B GGML support!
Thanks to the amazing efforts of
@apage43
, Jan Ploski et al at
Support is *experimental*. Won't work with UIs etc.
Here's two Falcon 40B models in GGML:
Pls read README!
@jayecreates
The larger models in the GPT4All ecosystem are not to bad at coding assistance. For code writing, we have optimized replits model to run on CPU and that will be joining the ecosystem when this PR is tested on all operating systems and merges.
- You can side-load almost any local LLM (GPT4All supports more than just LLaMa)
- Everything runs on CPU - yes it works on your computer!
- Dozens of developers actively working on it squash bugs on all operating systems and improve the speed and quality of models
GPT4All Townhall Announcement: 2024 Roadmap
Date: April 18th, 12pm EST
- Outline and discuss the 2024 Roadmap
- Opportunity for preview and feedback on GPT4All's next generation local LLM interface.
GPT-3 Embeddings by
@OpenAI
was announced this week.
📈 I was excited and tested them on 20 datasets
😢 Sadly they are worse than open models that are 1000 x smaller
💰 Running
@OpenAI
models can be a 1 million times more expensive
Previously, constant breaking changes to llama.cpp made it difficult to find software that works with any off-the-shelf local LLM.
The GPT4All ecosystem will now dynamically load the right versions without any intervention! LLMs should *just work*!
Our product GPT4All let's anyone run powerful large language models on resource constrained devices.
With this new funding, we are doubling down on our commitment to open-source and excited to accelerate work with our industry partners such as open-source giant
@MongoDB
.
GPT4All lets you run 13 officially supported models and side load hundreds from
@huggingface
. The following architectures are supported in GGML.
- LLaMA
- MPT
- Falcon
Follow-up on binary embeddings: 64 bytes per embedding, yee-haw 🤠
Reduces memory usage of our embedding model by more than 98% (64x) while retaining over 90% of model performance with binary 🪆
Model:
Blog:
Cool to see the Nomic shout out in
@StabilityAI
's StableLM readme!
I do hope they didn't fine tune directly on the chosen responses of the
@AnthropicAI
HH dataset though...
Announcing StableLM❗
We’re releasing the first of our large language models, starting with 3B and 7B param models, with 15-65B to follow. Our LLMs are released under CC BY-SA license.
We’re also releasing RLHF-tuned models for research use. Read more→
Current AI technology is concentrated in the hands of a very small number of companies with outsized access to compute resources. We are changing that:
Powerful models should be accessible to all:
The tools to build them as well:
Large Language Models like
#T5
and
#GPT3
are evaluated on benchmark datasets such as (super) GLUE. But what's in them?
Explore and search the contents of the GLUE benchmark with Atlas.
QNLI:
@sleepinyourhat
@EssentialBusin7
@llama
@MongoDB
Each dot is a piece of data. Two dots are close if they have similar embeddings. At Nomic we believe that it is organic - embeddings are a function of meaning, meaning is a function of culture, and culture is a function of organic animal behavior.