Trelis/Chorus-v1
Automatic Speech Recognition • 0.9B • Updated • 381 • 16
Technigma AI
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Trelis/Chorus-v1 liked a model 3 months ago
piotr-ai/Polanka_VL_v0.1_Qwen3_VL_4b_260120_gguf reacted to MonsterMMORPG's post with 🔥 4 months ago
Qwen Image Edit 2511 Free and Open Source Crushes Qwen Image Edit 2509 and Challenges Nano Banana Pro : https://www.youtube.com/watch?v=YfuQuOk2sB0
Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0
Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes
Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.
Here check some comparison imagesOrganizations
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reacted to MonsterMMORPG's post with 🔥 4 months ago
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Qwen Image Edit 2511 Free and Open Source Crushes Qwen Image Edit 2509 and Challenges Nano Banana Pro : https://www.youtube.com/watch?v=YfuQuOk2sB0
Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0
Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes
Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.
Here check some comparison images
Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0
Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes
Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.
Here check some comparison images
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reacted to AdamF92's post with 🔥 7 months ago
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Hi, I just published research paper that's introducing my Reactive Transformer (RxT) architecture. I would be grateful if you could check it and upvote on HuggingFace Daily Papers - Reactive Transformer (RxT) -- Stateful Real-Time Processing for
Event-Driven Reactive Language Models (2510.03561)
Architecture is based on stateful real-time processing with innovational asynchronous memory update. Instead of reprocessing all the conversation history for each message, it's processing only single query with all the context moved to dedicated memory layers. Memory is updated after generating the answer, so it's not influencing latency - in tests, time to first token was almost the same as generating a single token. It has also better quality/accuracy in multi-turn dialogue than the same size stateless decoder-only model.
Initial experiments were small scale (12M to 160M params models trained on simple synthetic datasets), but just now I'm starting training of bigger 270M params model on real data
Collection: ReactiveAI/reactive-transformer-poc-rxt-alpha-supervised-models-68e4004a4a59366e01a7b86f
Profile:
ReactiveAI
Architecture is based on stateful real-time processing with innovational asynchronous memory update. Instead of reprocessing all the conversation history for each message, it's processing only single query with all the context moved to dedicated memory layers. Memory is updated after generating the answer, so it's not influencing latency - in tests, time to first token was almost the same as generating a single token. It has also better quality/accuracy in multi-turn dialogue than the same size stateless decoder-only model.
Initial experiments were small scale (12M to 160M params models trained on simple synthetic datasets), but just now I'm starting training of bigger 270M params model on real data
Collection: ReactiveAI/reactive-transformer-poc-rxt-alpha-supervised-models-68e4004a4a59366e01a7b86f
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Q8_K_XL strange response
#2 opened 10 months ago
by technigmaai
GGUF version?
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#6 opened 10 months ago
by 
smcleod