Hugging Face's logo
Carlo Moro's picture
27 12 319

Carlo Moro

cnmoro
Gargaz's profile picture Bruno's profile picture John6666's profile picture
·
https://www.linkedin.com/in/carlo-moro-4a20a7132/

AI & ML interests

I like small & fast models Trabalhando nos menores modelos em português que existem https://ko-fi.com/cnmoro

Recent Activity

liked a model about 10 hours ago
teapotai/teapotllm
reacted to tomaarsen's post with 🔥 1 day ago
‼️Sentence Transformers v4.0 is out! You can now train and finetune reranker models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also prove that finetuning on your domain helps much more than you might think. 1️⃣ Reranker Training Refactor Reranker 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 - 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-reranker Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade. 2️⃣ New Reranker Losses - 11 new losses: - 2 traditional losses: BinaryCrossEntropy and CrossEntropy - 2 distillation losses: MSE and MarginMSE - 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL - 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE 3️⃣ New Reranker Documentation - New Training Overview, Loss Overview, API Reference docs - 5 new, 1 refactored training examples docs pages - 13 new, 6 refactored training scripts - Migration guides (2.x -> 3.x, 3.x -> 4.x) 4️⃣ Blogpost Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1
updated a dataset 1 day ago
cnmoro/reasoning-v1-20m-portuguese
View all activity

Organizations

Wise Intelligence's profile picture Smol Community's profile picture

cnmoro's activity

upvoted a collection 16 days ago
upvoted a paper 21 days ago
upvoted a collection about 2 months ago
upvoted a paper 2 months ago
upvoted an article 2 months ago
view article
Article

Train 400x faster Static Embedding Models with Sentence Transformers

• 164
upvoted a collection 4 months ago
upvoted 3 collections 9 months ago
upvoted a collection about 1 year ago
upvoted a collection over 1 year ago