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Decoding GPT-4'o': Its Mechanisms and Creating Similar AI.
๐ฅ๐ฒ๐ฎ๐ฑ ๐๐๐น๐น ๐๐ซ๐ญ๐ข๐๐ฅ๐: https://huggingface.co/blog/KingNish/decoding-gpt-4o
๐๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ ๐จ๐ ๐๐ซ๐ญ๐ข๐๐ฅ๐- ๐
# ๐๐๐๐ก๐๐ง๐ข๐๐ฌ ๐จ๐ ๐๐๐-๐โ๐จโ: GPT-4โoโ operates through three main components ๐ ๏ธ
๐. ๐๐ฎ๐ฉ๐๐ซ๐๐ก๐๐ญ: Integrates image generation, QnA (image, document and video) for diverse interactions.
๐. ๐๐จ๐ข๐๐ ๐๐ก๐๐ญ: Merges TTS and STT for real-time, human-like audio responses, focusing on human interaction.
๐. ๐๐ข๐๐๐จ ๐๐ก๐๐ญ: Utilizes Zero Shot Image Classification to enhance user interaction with visual information.
# ๐๐๐ญ๐ก๐จ๐๐ฌ ๐ญ๐จ ๐๐ซ๐๐๐ญ๐ ๐๐ข๐ฆ๐ข๐ฅ๐๐ซ ๐๐ ๐ง
๐. ๐๐ฎ๐ฅ๐ญ๐ข๐๐จ๐๐๐ฅ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง: Combines multiple models for a powerful, multifunctional AI.
๐. ๐๐ฎ๐๐ญ ๐๐๐ฉ๐ ๐๐๐ญ๐ก๐จ๐: Uses different models or APIs for specific tasks without additional training.
The article provides an in-depth exploration of GPT-4โoโ, its functionalities, and methods to create similar AI models. It emphasizes the modelโs language support and its innovative approach to human-AI interaction. ๐ก๐
(๐๐๐๐: ๐๐ช๐ข๐ข๐๐ง๐ฎ ๐๐จ ๐ผ๐ ๐๐๐ฃ๐๐ง๐๐ฉ๐๐) โ
๐ฅ๐ฒ๐ฎ๐ฑ ๐๐๐น๐น ๐๐ซ๐ญ๐ข๐๐ฅ๐: https://huggingface.co/blog/KingNish/decoding-gpt-4o
๐๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ ๐จ๐ ๐๐ซ๐ญ๐ข๐๐ฅ๐- ๐
# ๐๐๐๐ก๐๐ง๐ข๐๐ฌ ๐จ๐ ๐๐๐-๐โ๐จโ: GPT-4โoโ operates through three main components ๐ ๏ธ
๐. ๐๐ฎ๐ฉ๐๐ซ๐๐ก๐๐ญ: Integrates image generation, QnA (image, document and video) for diverse interactions.
๐. ๐๐จ๐ข๐๐ ๐๐ก๐๐ญ: Merges TTS and STT for real-time, human-like audio responses, focusing on human interaction.
๐. ๐๐ข๐๐๐จ ๐๐ก๐๐ญ: Utilizes Zero Shot Image Classification to enhance user interaction with visual information.
# ๐๐๐ญ๐ก๐จ๐๐ฌ ๐ญ๐จ ๐๐ซ๐๐๐ญ๐ ๐๐ข๐ฆ๐ข๐ฅ๐๐ซ ๐๐ ๐ง
๐. ๐๐ฎ๐ฅ๐ญ๐ข๐๐จ๐๐๐ฅ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง: Combines multiple models for a powerful, multifunctional AI.
๐. ๐๐ฎ๐๐ญ ๐๐๐ฉ๐ ๐๐๐ญ๐ก๐จ๐: Uses different models or APIs for specific tasks without additional training.
The article provides an in-depth exploration of GPT-4โoโ, its functionalities, and methods to create similar AI models. It emphasizes the modelโs language support and its innovative approach to human-AI interaction. ๐ก๐
(๐๐๐๐: ๐๐ช๐ข๐ข๐๐ง๐ฎ ๐๐จ ๐ผ๐ ๐๐๐ฃ๐๐ง๐๐ฉ๐๐) โ
- 2 replies
Consistency
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very true all has not yet been done there are still innovations ahead
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alielfilali01's
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Honestly i don't understand how come we as the open source community haven't surpassed GPT-4 yet ? Like for me it looks like everything is out there just need to be exploited! Clearly specialized small models outperforms gpt4 on downstream tasks ! So why haven't we just trained a 1B-2B really strong general model and then continue pertained and/or finetuned it on datasets for downstream tasks like math, code...well structured as Textbooks format or other datasets formats that have been proven to be really efficient and good! Ounce you have 100 finetuned model, just wrap them all into a FrankenMoE and Voila โจ
And that's just what a NOOB like myself had in mind, I'm sure there is better, more efficient ways to do it ! So the question again, why we haven't yet ? I feel I'm missing something... Right?
And that's just what a NOOB like myself had in mind, I'm sure there is better, more efficient ways to do it ! So the question again, why we haven't yet ? I feel I'm missing something... Right?
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2278
Mora
Enabling Generalist Video Generation via A Multi-Agent Framework
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework (2403.13248)
Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents.
Enabling Generalist Video Generation via A Multi-Agent Framework
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework (2403.13248)
Sora is the first large-scale generalist video generation model that garnered significant attention across society. Since its launch by OpenAI in February 2024, no other video generation models have paralleled {Sora}'s performance or its capacity to support a broad spectrum of video generation tasks. Additionally, there are only a few fully published video generation models, with the majority being closed-source. To address this gap, this paper proposes a new multi-agent framework Mora, which incorporates several advanced visual AI agents to replicate generalist video generation demonstrated by Sora. In particular, Mora can utilize multiple visual agents and successfully mimic Sora's video generation capabilities in various tasks, such as (1) text-to-video generation, (2) text-conditional image-to-video generation, (3) extend generated videos, (4) video-to-video editing, (5) connect videos and (6) simulate digital worlds. Our extensive experimental results show that Mora achieves performance that is proximate to that of Sora in various tasks. However, there exists an obvious performance gap between our work and Sora when assessed holistically. In summary, we hope this project can guide the future trajectory of video generation through collaborative AI agents.
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#3 opened almost 2 years ago
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rezalkretiva
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In Search of the Robust Facial Expressions Recognition Model: The Visual Cross-Corpus Study,
@ElenaRyumina
et al.
Model: ElenaRyumina/face_emotion_recognition
Demo: ElenaRyumina/Facial_Expression_Recognition
Repo: https://github.com/ElenaRyumina/EMO-AffectNetModel
Paper: https://www.sciencedirect.com/science/article/pii/S0925231222012656
@ElenaRyumina
Model: ElenaRyumina/face_emotion_recognition
Demo: ElenaRyumina/Facial_Expression_Recognition
Repo: https://github.com/ElenaRyumina/EMO-AffectNetModel
Paper: https://www.sciencedirect.com/science/article/pii/S0925231222012656
@ElenaRyumina
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#356 opened almost 2 years ago
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victor