openai/gpt-oss-20b
Text Generation
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computer vision, nlp , machine learning and deeplearning
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Qwen Image β The Latest Image Generation Modelπ₯
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. [ ππ ππ-πΈππππ : https://huggingface.co/Qwen/Qwen-Image ]
β€· Try the Qwen Image demo here: https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Diffusion, https://huggingface.co/spaces/Qwen/Qwen-Image & more ...
β€· Qwen-Image Technical Report : https://huggingface.co/papers/2508.02324
β€· 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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Qwen Image β The Latest Image Generation Modelπ₯
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. [ ππ ππ-πΈππππ : Qwen/Qwen-Image ]
β€· Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion, Qwen/Qwen-Image & more ...
β€· Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
β€· 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.
.
.
.
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. [ ππ ππ-πΈππππ : Qwen/Qwen-Image ]
β€· Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion, Qwen/Qwen-Image & more ...
β€· Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
β€· 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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π«‘ 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.π€πͺ
β€· Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
β€· Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
β€· Demo : prithivMLmods/core-OCR
Multimodal Model Collections and Spaces:
β Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
β Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
β Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
β 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. !!
β€· Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
β€· Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
β€· Demo : prithivMLmods/core-OCR
Multimodal Model Collections and Spaces:
β Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
β Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
β Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
β 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 π₯ an upgrade to the non-thinking mode model
Qwen/Qwen3-30B-A3B-Instruct-2507
β¨ 30B MoE / 3.3B active - Apache 2.0
β¨ Strong gains in reasoning, math, coding, & multilingual tasks
β¨ Native support for 256K long-context inputs
Qwen/Qwen3-30B-A3B-Instruct-2507
β¨ 30B MoE / 3.3B active - Apache 2.0
β¨ Strong gains in reasoning, math, coding, & multilingual tasks
β¨ Native support for 256K long-context inputs
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KAT-V1 π₯ a LLM that tackles overthinking by switching between reasoning and direct answers, by Kuaishou.
Kwaipilot/KAT-V1-40B
β¨ 40B
β¨ Step-SRPO: smarter reasoning control via RL
β¨ MTP + Distillation: efficient training, lower cost
Kwaipilot/KAT-V1-40B
β¨ 40B
β¨ Step-SRPO: smarter reasoning control via RL
β¨ MTP + Distillation: efficient training, lower cost
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