google/translategemma-27b-it
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google/translategemma-27b-it
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Lightricks/ltx-2
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Qwen3-Next can now be Run locally! (30GB RAM)
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
๐ Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
Instruct GGUF: unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
The models come in Thinking and Instruct versions and utilize a new architecture, allowing it to have ~10x faster inference than Qwen32B.
๐ Step-by-step Guide: https://docs.unsloth.ai/models/qwen3-next
Thinking GGUF: unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
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NexaAI/Qwen2-Audio-7B-GGUF
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unsloth/Qwen3-VL-4B-Thinking-GGUF
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ggml-org/Qwen3-VL-2B-Instruct-GGUF
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ggml-org/LightOnOCR-1B-1025-GGUF
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deepseek-ai/DeepSeek-OCR is out! ๐ฅ my take โคต๏ธ
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
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The POINTS-Reader, a vision-language model for end-to-end document conversion, is a powerful, distillation-free Vision-Language Model that sets new SoTA benchmarks. The demo is now available on HF (Extraction, Preview, Documentation). The input consists of a fixed prompt and a document image, while the output contains only a string (the text extracted from the document image). ๐ฅ๐ค
โฆ Space/App: prithivMLmods/POINTS-Reader-OCR
โฆ Model: tencent/POINTS-Reader
โฆ Paper: https://arxiv.org/pdf/2509.01215
๐ค The app is done and ready to go brrrr with zero GPU. Thankyou @merve
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To know more about it, visit the app page or the respective model page!!
โฆ Space/App: prithivMLmods/POINTS-Reader-OCR
โฆ Model: tencent/POINTS-Reader
โฆ Paper: https://arxiv.org/pdf/2509.01215
๐ค The app is done and ready to go brrrr with zero GPU. Thankyou @merve
.
.
.
To know more about it, visit the app page or the respective model page!!
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