Empowering Open-Source Artificial Intelligence Research

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LangChain is a framework for developing applications powered by language models. It enables applications that:

Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
This framework consists of several parts.

LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
LangServe: A library for deploying LangChain chains as a REST API.
LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.

Magic is working on frontier-scale code models to build a coworker, not just a copilot.

With LM Studio, you can ...
🤖 - Run LLMs on your laptop, entirely offline
👾 - Use models through the in-app Chat UI or an OpenAI compatible local server
📂 - Download any compatible model files from HuggingFace 🤗 repositories
🔭 - Discover new & noteworthy LLMs in the app's home page

Noticias y datos estádisticos sobre IA. Enfocado en regulaciones

This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface.

Swirl is open-source software that uses AI to simultaneously search multiple content and data sources and return AI ranked results which can be used to prompt Generative AI, enabling you to get answers based on your own data.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality, etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing.