openai/whisper-large-v3-turbo
Automatic Speech Recognition โข 0.8B โข Updated โข 6.43M โข โข 2.92k
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๐ The Ghost 8B Beta model outperforms prominent models such as Llama 3 8B Instruct, GPT 3.5 Turbo in the lc_winrate score. In addition, it also outperforms Claude 3 Opus, Claude 3 Sonnet, GPT-4, and Mistral Large when comparing the winrate score of AlpacaEval 2.0.
Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
The languages supported are ๐บ๐ธ English, ๐ซ๐ท French, ๐ฎ๐น Italian, ๐ช๐ธ Spanish, ๐ต๐น Portuguese, ๐ฉ๐ช German, ๐ป๐ณ Vietnamese, ๐ฐ๐ท Korean and ๐จ๐ณ Chinese.
Explore the Potential:
To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:
- Ghost 8B Beta (ฮฒ, 8k) on Spaces: lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (ฮฒ, 128k) on Spaces: lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta
Ghost 8B Beta is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default.
The languages supported are ๐บ๐ธ English, ๐ซ๐ท French, ๐ฎ๐น Italian, ๐ช๐ธ Spanish, ๐ต๐น Portuguese, ๐ฉ๐ช German, ๐ป๐ณ Vietnamese, ๐ฐ๐ท Korean and ๐จ๐ณ Chinese.
Explore the Potential:
To learn more about this groundbreaking language model, visit the official website or explore the online demo platforms:
- Ghost 8B Beta (ฮฒ, 8k) on Spaces: lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (ฮฒ, 128k) on Spaces: lamhieu/ghost-8b-beta-128k
- Official website: https://ghost-x.org/docs/models/ghost-8b-beta
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Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models
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New open Vision Language Model by @Google : PaliGemma ๐๐ค
๐ Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
๐งฉ Combination of Gemma 2B LLM and SigLIP image encoder
๐ค Supported in transformers
PaliGemma can do..
๐งฉ Image segmentation and detection! ๐คฏ
๐ Detailed document understanding and reasoning
๐ Visual question answering, captioning and any other VLM task!
Read our blog ๐ hf.co/blog/paligemma
Try the demo ๐ช hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection ๐ google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda
๐ Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
๐งฉ Combination of Gemma 2B LLM and SigLIP image encoder
๐ค Supported in transformers
PaliGemma can do..
๐งฉ Image segmentation and detection! ๐คฏ
๐ Detailed document understanding and reasoning
๐ Visual question answering, captioning and any other VLM task!
Read our blog ๐ hf.co/blog/paligemma
Try the demo ๐ช hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection ๐ google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda
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Decoding GPT-4'o': In-Depth Exploration of Its Mechanisms and Creating Similar AI.
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๐๐ค๐ New Research Alert - CVPR 2024! ๐๐ค๐
๐ Title: RoHM: Robust Human Motion Reconstruction via Diffusion ๐
๐ Description: RoHM is a diffusion-based approach for robust 3D human motion reconstruction from monocular RGB(-D) videos, effectively handling noise and occlusions to produce complete and coherent motions. This method outperforms current techniques in various tasks and is faster at test time.
๐ฅ Authors: Siwei Zhang et al.
๐ Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)
๐ GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
๐ Repository: https://github.com/sanweiliti/RoHM
๐ Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation
๐ Title: RoHM: Robust Human Motion Reconstruction via Diffusion ๐
๐ Description: RoHM is a diffusion-based approach for robust 3D human motion reconstruction from monocular RGB(-D) videos, effectively handling noise and occlusions to produce complete and coherent motions. This method outperforms current techniques in various tasks and is faster at test time.
๐ฅ Authors: Siwei Zhang et al.
๐ Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA ๐บ๐ธ
๐ Paper: RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)
๐ GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
๐ Repository: https://github.com/sanweiliti/RoHM
๐ Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation