ausboss/Qwen-Image-2.1-Outfit-Swap-Consistency-LoRA
Image-to-Image β’ Updated β’ 153
optimalprime
optimalprime
AI & ML interests
Python, OCR, ASR, video processing
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liked a model about 1 month ago
ausboss/Qwen-Image-2.1-Outfit-Swap-Consistency-LoRA liked a Space 4 months ago
TheStinger/UVR5_UI new activity 8 months ago
multimodalart/Foundation-1:Otputs are heavilly distorted.Organizations
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Otputs are heavilly distorted.
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#1 opened 8 months ago
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WaveCut
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DIdn't get Inference Provider credits
ππ€ 1
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#1 opened 10 months ago
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Anulo2
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upvoted an article about 1 year ago
Article
Smol2Operator: Post-Training GUI Agents for Computer Use
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A-Mahla, merve, sergiopaniego, reach-vb, lewtun
β’ β’ 139 upvoted a paper about 1 year ago
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reacted to tomaarsen's post with β€οΈ about 1 year ago
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βΌοΈSentence Transformers v3.0 is out! You can now train and finetune embedding models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also release 50+ datasets to train on.
1οΈβ£ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2οΈβ£ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
3οΈβ£ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4οΈβ£ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5οΈβ£ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
1οΈβ£ Training Refactor
Embedding models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Improved model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-sentence-transformers
2οΈβ£ Similarity Score
Not sure how to compare embeddings? Don't worry, you can now use
model.similarity(embeddings1, embeddings2) and you'll get your similarity scores immediately. Model authors can specify their desired similarity score, so you don't have to worry about it anymore!3οΈβ£ Additional Kwargs
Sentence Transformers relies on various Transformers instances (AutoModel, AutoTokenizer, AutoConfig), but it was hard to provide valuable keyword arguments to these (like 'torch_dtype=torch.bfloat16' to load a model a lower precision for 2x inference speedup). This is now easy!
4οΈβ£ Hyperparameter Optimization
Sentence Transformers now ships with HPO, allowing you to effectively choose your hyperparameters for your data and task.
5οΈβ£ Dataset Release
To help you out with finetuning models, I've released 50+ ready-to-go datasets that can be used with training or finetuning embedding models: sentence-transformers/embedding-model-datasets-6644d7a3673a511914aa7552
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.0
reacted to freddyaboulton's post with π₯ about 1 year ago
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Getting WebRTC and Websockets right in python is very tricky. If you've tried to wrap an LLM in a real-time audio layer then you know what I'm talking about.
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
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reacted to MrDragonFox's post with π over 1 year ago
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as a few of you know - i am working on a rather more elaborate-tts that can produce more interesting sounds in context of rp
early sneak peak is here -
MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
its based on orpheus - but really the model is irrelevant as i focus mostly on data augmentation / prep / pipelineing - its just the way to show progress
should be able to express fine even in a sfw context
probably the last release for a few weeks as i go back to the data pipeline and improve there ..
in the mean time, please do test and report problems or enjoyable generations you found - we have a growing discord community and i love to see what you get out of that early release !
(small colab is provided on the model page if you dont have the gpu to run that your self)
early sneak peak is here -
MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
its based on orpheus - but really the model is irrelevant as i focus mostly on data augmentation / prep / pipelineing - its just the way to show progress
should be able to express fine even in a sfw context
probably the last release for a few weeks as i go back to the data pipeline and improve there ..
in the mean time, please do test and report problems or enjoyable generations you found - we have a growing discord community and i love to see what you get out of that early release !
(small colab is provided on the model page if you dont have the gpu to run that your self)
reacted to orasul's post with π₯ over 1 year ago
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hi, it is deki, and now I am open sourced.
An Android AI agent powered by open-source ML model, π±π²πΈπΆ, was fully open-sourced.
It understands whatβs on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Currently, it works only on Android β but support for other OS is planned.
The ML and backend codes were also fully open-sourced.
Video prompt example:
"Open linkedin, tap post and write: hi, it is deki, and now I am open sourced. But don't send, just return"
License: GPLv3
You can find other AI agent demos or usage examples, like, code generation or object detection in github.
Github: https://github.com/RasulOs/deki
An Android AI agent powered by open-source ML model, π±π²πΈπΆ, was fully open-sourced.
It understands whatβs on your screen and can perform tasks based on your voice or text commands.
Some examples:
* "Write my friend "some_name" in WhatsApp that I'll be 15 minutes late"
* "Open Twitter in the browser and write a post about something"
* "Read my latest notifications"
* "Write a linkedin post about something"
Currently, it works only on Android β but support for other OS is planned.
The ML and backend codes were also fully open-sourced.
Video prompt example:
"Open linkedin, tap post and write: hi, it is deki, and now I am open sourced. But don't send, just return"
License: GPLv3
You can find other AI agent demos or usage examples, like, code generation or object detection in github.
Github: https://github.com/RasulOs/deki
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reacted to AdinaY's post with π₯ over 1 year ago
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Kimi-Audio ππ§ an OPEN audio foundation model released by Moonshot AI
moonshotai/Kimi-Audio-7B-Instruct
β¨ 7B
β¨ 13M+ hours of pretraining data
β¨ Novel hybrid input architecture
β¨ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)
moonshotai/Kimi-Audio-7B-Instruct
β¨ 7B
β¨ 13M+ hours of pretraining data
β¨ Novel hybrid input architecture
β¨ Universal audio capabilities (ASR, AQA, AAC, SER, SEC/ASC, end-to-end conversation)