TellWorldAI World Archive

Home›Models

Models

Models

Foundation, generative, multimodal and specialised models.

Total 37 · Clear filters

2026

2025

  • May 1, 2025 Falcon-H1

    Falcon-H1 is a family of open-weight language models that runs Transformer attention and state space layers in parallel, released in sizes from 0.5B to 34B parameters under the Falcon-LLM Licence.

    Large language modelsOpen sourceFoundation models

  • Mar 18, 2025 NVIDIA Isaac GR00T N1

    Isaac GR00T N1 is an open foundation model for humanoid robots that NVIDIA announced on 18 March 2025. It is designed to be post-trained for a specific robot or task, and it pairs a planning model with quick motion control.

    Open sourceFoundation modelsRobotics

  • Mar 12, 2025 Gemini Robotics

    Gemini Robotics is a vision-language-action model from Google DeepMind, built on Gemini 2.0, that outputs physical actions to control robots. It was announced on 12 March 2025 alongside Gemini Robotics-ER.

    MultimodalRoboticsComputer vision

  • Mar 10, 2025 AgiBot GO-1 (Genie Operator-1)

    A general-purpose embodied foundation model released by AgiBot on 10 March 2025, built on a Vision-Language-Latent-Action architecture. In the company's tests across five tasks it raised average success rate from 46% to 78%.

    ChinaFoundation modelsMultimodal

  • Feb 20, 2025 Figure Helix

    Helix is Figure's vision-language-action model for humanoid control, pairing a 7-billion-parameter vision-language model with a fast 80-million-parameter action policy, trained on about 500 hours of teleoperated data and run entirely on onboard GPUs.

    Deep learningFoundation modelsMultimodal

2024

2023

2022

2021

  • Dec 8, 2021 Gopher

    Gopher is DeepMind's 280-billion-parameter language model, announced in December 2021 alongside a study of what scale does to language models and a companion taxonomy of their risks.

    Large language modelsResearch historyEurope

  • Oct 11, 2021 Megatron-Turing NLG 530B

    Megatron-Turing NLG 530B is a 530-billion-parameter language model announced by NVIDIA and Microsoft on 11 October 2021, then the largest monolithic transformer language model, trained on NVIDIA's Selene supercomputer with Megatron-LM and DeepSpeed combined.

    Large language modelsFoundation modelsHardware & compute

  • Jul 5, 2021 ERNIE 3.0

    ERNIE 3.0 is a 10-billion-parameter pre-trained language model from 2021 that trains on plain text plus a large-scale knowledge graph and fuses auto-encoding and auto-regressive networks, so one model handles both language understanding and generation.

    Large language modelsResearch historyChina

  • May 18, 2021 LaMDA

    LaMDA is a family of conversational language models that Google announced in 2021 and described in a 2022 paper, notable for its work on safety and factual grounding in dialogue and for later powering Bard.

    Large language modelsResearch historyConsumer apps

  • Jan 5, 2021 CLIP

    An image-text model from OpenAI that learns to match pictures with their captions. Trained on about 400 million image-caption pairs, it can classify images by comparing them against written prompts, with no task-specific training.

    Open sourceFoundation modelsMultimodal

  • Jan 5, 2021 DALL·E

    DALL·E is OpenAI's text-to-image model unveiled on 5 January 2021: a 12-billion-parameter transformer trained on 250 million text-image pairs that generates a picture token by token from a caption.

    MultimodalGenerative modelsCreative tools

2020

  • May 28, 2020 GPT-3

    GPT-3 is an autoregressive language model with 175 billion parameters, described in a May 2020 paper. It could perform new tasks from a few examples in the prompt, without any gradient updates, and it was first offered through OpenAI's API.

    Large language modelsResearch historyFoundation models

  • Jan 28, 2020 Meena

    Meena is a 2.6-billion-parameter open-domain chatbot from Google, trained end-to-end on filtered social media conversations. Its 2020 paper introduced the SSA human-evaluation metric and linked next-token perplexity to human-judged chat quality. The work was later renamed LaMDA.

    Large language modelsResearch historyConsumer apps

2019

2018

  • Oct 11, 2018 BERT

    BERT is a language model from Google that reads text in both directions and is pre-trained by filling in masked words. Fine-tuned per task, it set new records on question answering and language-understanding benchmarks and later went into Google Search.

    Large language modelsResearch historyOpen source

  • Jun 11, 2018 GPT-1

    GPT-1 was OpenAI's first generative pre-trained transformer, published in June 2018. It showed that one language model, pre-trained on unlabelled text and then fine-tuned, could improve results on many language tasks.

    Large language modelsResearch historyOpen source