Qwen/Qwen-Image
Text-to-Image
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computer vision, nlp , machine learning and deeplearning
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Lately, we've been experimenting with recommending arXiv papers based on the context of what we're building in AI.
At the same time, we're using an agent to help automate the building and testing of Docker Images.
Check out the example here:
https://hub.docker.com/repository/docker/remyxai/2507.20613v1/general
Next, we're tasking our #ExperimentOps agent to open PRs in a target repo, to evaluate the core concepts from a new research paper in the context of your application and your kpis.
Operationalize your Experimentation!
Find Your Frontier!
#BeAnExperimenter
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๐ฃ๐ฎ๐ฝ๐ฒ๐ฟ๐ฎ๐ฃ๐ฅ๐
Lately, we've been experimenting with recommending arXiv papers based on the context of what we're building in AI.
At the same time, we're using an agent to help automate the building and testing of Docker Images.
Check out the example here:
https://hub.docker.com/repository/docker/remyxai/2507.20613v1/general
Next, we're tasking our #ExperimentOps agent to open PRs in a target repo, to evaluate the core concepts from a new research paper in the context of your application and your kpis.
Operationalize your Experimentation!
Find Your Frontier!
#BeAnExperimenter
Lately, we've been experimenting with recommending arXiv papers based on the context of what we're building in AI.
At the same time, we're using an agent to help automate the building and testing of Docker Images.
Check out the example here:
https://hub.docker.com/repository/docker/remyxai/2507.20613v1/general
Next, we're tasking our #ExperimentOps agent to open PRs in a target repo, to evaluate the core concepts from a new research paper in the context of your application and your kpis.
Operationalize your Experimentation!
Find Your Frontier!
#BeAnExperimenter
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Qwen Image โ The Latest Image Generation Model๐ฅ
Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source. [ ๐๐ ๐๐-๐ธ๐๐๐๐ : Qwen/Qwen-Image ]
โคท Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion
โคท Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
โคท Qwen Image [GitHub] : https://github.com/QwenLM/Qwen-Image
Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery.
Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.
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To know more about it, visit the model card of the respective model. !!
Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source. [ ๐๐ ๐๐-๐ธ๐๐๐๐ : Qwen/Qwen-Image ]
โคท Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion
โคท Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
โคท Qwen Image [GitHub] : https://github.com/QwenLM/Qwen-Image
Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery.
Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.
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To know more about it, visit the model card of the respective model. !!
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๐ซก I am the first and only one to like the French Tax Code Dataset
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
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Introducing Camel-Doc-OCR-080125(v2), a document content-structure retrieval VLM designed for content extraction and summarization. This is the second model in the Camel Doc OCR VLM series, following Camel-Doc-OCR-062825(v1). The new version fixes formal table reconstruction issues in both en and zh language, achieving optimal performance for long-context inferences.๐ค๐ช
โคท Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
โคท Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
โคท Demo : prithivMLmods/core-OCR
Multimodal Model Collections and Spaces:
โ Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
โ Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
โ Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
โ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
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To know more about it, visit the model card of the respective model. !!
โคท Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
โคท Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
โคท Demo : prithivMLmods/core-OCR
Multimodal Model Collections and Spaces:
โ Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
โ Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
โ Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
โ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
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To know more about it, visit the model card of the respective model. !!
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Qwen just released Qwen3-30B-A3B-Instruct-2507 ๐ฅ an upgrade to the non-thinking mode model
Qwen/Qwen3-30B-A3B-Instruct-2507
โจ 30B MoE / 3.3B active - Apache 2.0
โจ Strong gains in reasoning, math, coding, & multilingual tasks
โจ Native support for 256K long-context inputs
Qwen/Qwen3-30B-A3B-Instruct-2507
โจ 30B MoE / 3.3B active - Apache 2.0
โจ Strong gains in reasoning, math, coding, & multilingual tasks
โจ Native support for 256K long-context inputs
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KAT-V1 ๐ฅ a LLM that tackles overthinking by switching between reasoning and direct answers, by Kuaishou.
Kwaipilot/KAT-V1-40B
โจ 40B
โจ Step-SRPO: smarter reasoning control via RL
โจ MTP + Distillation: efficient training, lower cost
Kwaipilot/KAT-V1-40B
โจ 40B
โจ Step-SRPO: smarter reasoning control via RL
โจ MTP + Distillation: efficient training, lower cost
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