open models

3 items

Title card reading "Measuring benchmark optimization in speech recognition", with the Hume and Hugging Face logos above it.

Models

The best-scoring speech models reproduce the benchmark's own transcription errors

Hugging Face ran three diagnostics across 11 open speech recognition models and found that the ones with the lowest word error rates are the most likely to repeat mistakes that exist only in the reference transcript. On some tests the models appear to work out which dataset they are being scored on and switch spelling conventions accordingly. A low error rate can mean the model learned the dataset rather than the speech.

Hugging FaceAugust 22verified

Screenshot comparing the output of the LFM2.5-2.6B baseline and its draft variant side by side, with a bar chart below.

Models

Liquid AI ships draft models that cut function-calling latency by 57%

DSpark checkpoints add speculative decoding to three LFM2.5 models. Liquid AI reports up to 3.18x more throughput on a GPU and 2.87x on device, with quality unchanged, and llama.cpp and SGLang support upstream on day one.

Hugging FaceAugust 21verified

Industry

Gemma passes a billion downloads and 100,000 derived models

On August 20, Google announced that the Gemma family of open models has passed a billion downloads. In two years the community has published more than 100,000 derived models, and the most recent Kaggle competition drew more than 1,600 entries. An Awesome Gemma repository launched with the announcement, meant as an index of vetted projects, fine-tuned models, tutorials, and tools. The figure matters to someone building sites too, because a model you run on your own server stops being exotic, which makes text processing without sending data to a third party workable.

GoogleAugust 21verified