Misc models: 🦖T-Rex2, a very powerful object detection model for many applications https://github.com/IDEA-Research/T-Rex 👀 CT-RATE : A 3D dataset paired with text reports ibrahimhamamci/CT-RATE 🐙Octopus v2: a Gemma-based model trained for Android API - extremely fast, better than Llama+RAG, great results NexaAIDev/Octopus-v2
🌏Models and datasets around the world - Tess-70B, a MiQu-70B fine-tune with high-quality data migtissera/Tess-70B-v1.6 - UNI, a model trained on 100 million pathology images from 100k+ slides MahmoodLab/UNI - CONCH, a VLM trained on 1.17 million pathology image-text pairs MahmoodLab/CONCH
5. SpeechBrain 1.0: a toolkit with hundreds of recipes and pretrained models for audio-related tasks, such as speech recognition, diarization, and enhancement. New major release! HF repos: https://huggingface.co/speechbrain Website: https://speechbrain.github.io/
The community has struggled to do a good preference-tune of Gemma, so the amazing @lewtun and @philschmid built an open-source recipe and trained a model to help people get started.
Some interesting details - Fine-tuned on DEITA and DPOed with Argilla DPO dataset - Very strong MT Bench results (7.81), better than Zephyr Beta (mistral based) and Gemma Instruct - Can run locally with tools such as llama.cpp on a Mac - Not so good AGIEval results compared to mistral-based tunes - All training code is open-sourced - Trained for 105 minutes on 8x H100 - No system message
Big kudos to the team! Super exciting to see a good fine-tune for Gemma