Hugging Face's logo
Damar Jati šŸ«'s picture
25 6 191

Damar Jati šŸ«

DamarJati
eversonjp's profile picture Tonic's profile picture yazoka211's profile picture
Ā·
https://damarcreative.my.id

AI & ML interests

Indonesian - Multimodal, Compvis, NLP | Discord: @damarjati_

Recent Activity

liked a model about 6 hours ago
cagliostrolab/animagine-xl-4.0-zero
reacted to ZennyKenny's post with šŸ”„ 2 days ago
I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.
reacted to tomaarsen's post with šŸ”„ 3 days ago
šŸ“£ Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost. 1ļøāƒ£ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference. 2ļøāƒ£ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU. Usage is as simple as `SentenceTransformer("all-MiniLM-L6-v2", backend="onnx")`. Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later šŸ˜‰ šŸ”’ Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways: 1ļøāƒ£ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with `from_model2vec` or with `from_distillation` where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed. 2ļøāƒ£ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU. Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0 Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
View all activity

Organizations

DeepGHS's profile picture Stable Diffusion Dreambooth Concepts Library's profile picture Blog-explorers's profile picture Tensor Diffusion's profile picture That Time I got Reincarnated as a Hugging Face Organization's profile picture ZeroGPU Explorers's profile picture Cagliostro Lab Dataset's profile picture Cagliostro Research Lab's profile picture Social Post Explorers's profile picture Dev Mode Explorers's profile picture Ani Community's profile picture Hugging Face Discord Community's profile picture Cagliostrolab Archive's profile picture

Posts 2

view post
Post
2745
Happy New Year 2025 šŸ¤—
For the Huggingface community.
view post
Post
4131
Improved ControlNet!
Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortionā€”optimized for any aspect ratio!
...
DamarJati/FLUX.1-DEV-Canny
View all Posts

Collections 4