LLMs
In addition to using our built-in capabilities, you can also define custom actions by making one or more APIs available to the GPT. Like plugins, actions allow GPTs to integrate external data or interact with the real-world. Connect GPTs to databases, plug them into emails, or make them your shopping assistant. For example, you could integrate a... See more
Introducing GPTs
When we deliver a model we make sure we don't reach X seconds of latency in our API. Before even going into performance of LLMs for classification, I can tell you that with the current available tech they are just infeasible.
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LinuxSpinach
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5h ago
^ this. And especially classification as a task, because businesses donโt want to pay llm... See more
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LinuxSpinach
โข
5h ago
^ this. And especially classification as a task, because businesses donโt want to pay llm... See more
r/MachineLearning - Reddit
We're doing NER on hundreds of millions of documents in a specialised niche. LLMs are terrible for this. Slow, expensive and horrifyingly inaccurate. Even with agents, pydantic parsing and the like. Supervised methods are the way to go. Hell, I'd take an old school rule based approach over LLMs for this.
๐ฑ๐ฆ๐ณ๐ง๐ฐ๐ณ๐ฎ๐ข๐ฏ๐ค๐ฆ: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)
๐ค๐ฐ๐ฏ๐ต๐ณ๐ฐ๐ญ: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product
๐ฎ๐ฐ๐ฅ๐ถ๐ญ๐ข๐ณ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ:... See more
๐ค๐ฐ๐ฏ๐ต๐ณ๐ฐ๐ญ: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product
๐ฎ๐ฐ๐ฅ๐ถ๐ญ๐ข๐ณ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ:... See more
Shortwave โ rajhesh.panchanadhan@gmail.com [Gmail alternative]
Motivation for finetuning
TorchMultimodal (Beta Release)
Introduction
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:
Introduction
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. It provides:
- A repository of modular and composable building blocks (models, fusion layers, loss functions, datasets and utilities).
- A repository of examples that show how to combine these building
facebookresearch โข GitHub - facebookresearch/multimodal at a33a8b888a542a4578b16972aecd072eff02c1a6
You can think your way into solving a deterministic system, but you cannot think your way into solving a probabilistic system.
The first thing that I want to call out is that deterministic software has edge cases, while probabilistic software has long tails.
I find that a lot of junior folks try to really think hard about edge cases around... See more
Jason Liu โข Tips for probabilistic software - jxnl.co
What is Substrate?
Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models โ tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more
Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models โ tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more
Nextra: the next docs builder
Mem0: The Memory Layer for Personalized AI
Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.
Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain โค๏ธ. You can find the Embedchain codebase in the embedchai... See more
GitHub - mem0ai/mem0: The memory layer for Personalized AI
weโre in a capability overhang - the AI tech that already exists has huge potential impact, whether you engage or not, so get ahead by exploring
the appropriate approach is pathfinding which uses experiments to learn and, critically, artefacts to tell the organisation what to do next.
the appropriate approach is pathfinding which uses experiments to learn and, critically, artefacts to tell the organisation what to do next.