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Next big thing after LLMs - World Model [explained on the example of V-JEPA2]

Elon is melting down

What's the smartest tiny LLM you've actually used?

Mistral AI_

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Voxtral-Mini-3B-2507

Task: audio-text-to-text

Mistral AI_

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Voxtral-Small-24B-2507

Task: audio-text-to-text

NVIDIA

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canary-qwen-2.5b

Task: automatic-speech-recognition

Moonshot AI

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Kimi-K2-Base

Task: text-generation

Liquid AI

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LFM2-1.2B

Task: text-generation

ilkerizgi

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Overlay-Kontext-Dev-LoRA

Task: image-to-image

DeepSeek

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DeepSeek-R1-0528

Google

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gemma-3n-E4B-it

Task: image-text-to-text

Google

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medgemma-27b-it

Task: image-text-to-text

tracel-ai

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burn

Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.

n8n-io

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n8n

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

huggingface

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transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

coqui-ai

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TTS

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

langchain-ai

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langchain

🦜🔗 Build context-aware reasoning applications

Project-MONAI

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MONAI

AI Toolkit for Healthcare Imaging

Project-HAMi

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HAMi

Heterogeneous AI Computing Virtualization Middleware(Project under CNCF)

microsoft

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onnxruntime

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Deci-AI

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super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.