dllm-collection/ModernBERT-large-chat-v0.1
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š 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 šŗšø English, š«š· French, š®š¹ Italian, šŖšø Spanish, šµš¹ Portuguese, š©šŖ German, š»š³ Vietnamese, š°š· Korean and šØš³ 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 (Ī², 8k) on Spaces: lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (Ī², 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 šŗšø English, š«š· French, š®š¹ Italian, šŖšø Spanish, šµš¹ Portuguese, š©šŖ German, š»š³ Vietnamese, š°š· Korean and šØš³ 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 (Ī², 8k) on Spaces: lamhieu/ghost-8b-beta-8k.
- Ghost 8B Beta (Ī², 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
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: PaliGemma šš¤
š Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
š§© Combination of Gemma 2B LLM and SigLIP image encoder
š¤ Supported in transformers
PaliGemma can do..
š§© Image segmentation and detection! š¤Æ
š Detailed document understanding and reasoning
š Visual question answering, captioning and any other VLM task!
Read our blog š hf.co/blog/paligemma
Try the demo šŖ hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection š google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models google/paligemma-ft-models-6643b03efb769dad650d2dda
š Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
š§© Combination of Gemma 2B LLM and SigLIP image encoder
š¤ Supported in transformers
PaliGemma can do..
š§© Image segmentation and detection! š¤Æ
š Detailed document understanding and reasoning
š Visual question answering, captioning and any other VLM task!
Read our blog š hf.co/blog/paligemma
Try the demo šŖ hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection š 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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šš¤š New Research Alert - CVPR 2024! šš¤š
š Title: RoHM: Robust Human Motion Reconstruction via Diffusion š
š 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.
š„ Authors: Siwei Zhang et al.
š Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA šŗšø
š Paper: RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)
š GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
š Repository: https://github.com/sanweiliti/RoHM
š Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
š More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
š Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation
š Title: RoHM: Robust Human Motion Reconstruction via Diffusion š
š 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.
š„ Authors: Siwei Zhang et al.
š Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA šŗšø
š Paper: RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)
š GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
š Repository: https://github.com/sanweiliti/RoHM
š Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
š More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
š Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation
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