I just shared a blogpost on https://nateraw.com explaining the motivation + process of training nateraw/musicgen-songstarter-v0.2 - including training details, WandB logs, hparams, and notes on previous experiments.
It'll take your voice and try to autotune it (because let's be real, you're no michael jackson), then pass it along to the model to condition on the melody. It works surprisingly well!
🌏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: 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