Open weights go compact: Phonon-2, Kumo Tabular and a volunteer-trained model ship

Three open-weight releases landed inside a single day, and none of them chased scale. Fermion Research published Phonon-2 on 30 September, an English speech recognition model that stores each encoder weight as one of five learned levels at about 2.1 bits, in a 164 MB file. The company says it matches its 2.5 GB full-precision teacher set for set, beats it on meetings and parliamentary speech, and transcribes an hour of audio in about 20 seconds on a MacBook Air. The weights are on Hugging Face under CC-BY-4.0, the licence of Nvidia's Parakeet TDT 0.6B v3, from which they derive.

That last detail matters. The small open-model race is largely a race of teachers and licences.

Fermion claims every open model that scores better on the Open ASR Leaderboard's seven English sets is at least 5.8 times its size, and that the lead holds under noise, staying ahead of Parakeet Redux, the other low-bit model of its size. Speed figures come with conditions attached: one clip at a time, log-mel included, model load excluded, and each fast path ships only if its word error stays within noise of the exact path under a paired bootstrap over clips with 4,000 resamples.

Kumo Tabular attacks the tree-model lifecycle

Nvidia published Kumo Tabular on 29 September, an open foundation model for tabular classification and regression that predicts new rows in a single forward pass with no training, tuning or feature engineering. It comes in three sizes from 28M to 215M parameters, is released under the OpenMDW-1.1 licence for commercial use, and ranks first on four benchmarks: TabArena, BeyondArena, TALENT and ScoringBench, according to the Hugging Face post.

The architecture borrows from TabICL and TabPFN, with column, row and in-context attention. The company says it was pretrained only on artificial data, which sidesteps the privacy problem of training on customer tables. Whether it unseats gradient-boosted trees in production is a separate question from benchmark placement, and Nvidia's post does not answer it.

A language model trained on GitHub Actions and donated hardware

The Coop project, a GitHub repository that went live on 30 September, is attempting something stranger: pretraining a small language model with no servers and no funding. Stage 2 is a roughly 145M-parameter decoder-only transformer training from scratch on FineWeb-Edu. Workers download a checkpoint from Hugging Face, run local AdamW steps on a personal data shard, and submit a pseudo-gradient as a pull request against a public dataset repository. A stateless GitHub Actions cron job, scheduled every five minutes, aggregates the submissions with a trimmed mean or geometric median and takes one Nesterov outer step.

Stage 1 is the proof: a 15M-parameter model trained past its Chinchilla-optimal budget by volunteers in six days, with validation loss falling from 9.01 to 2.8. The weights stay available. The project's own documentation is unusually candid about evaluation, noting that a single validation loss number says nothing because outer steps move it up as often as down, and that the direction is a property of the series, not one point.

Two other open releases rounded out the window. The UK AI Security Institute and Meridian Labs published Inspect, an evaluation framework with over 200 pre-built evaluations and sandboxing for untrusted model code in Docker, Kubernetes and Modal. And Sparticle62ops released PSSA, a non-transformer language model written in Rust with no ML framework underneath, claiming it learns faster than a transformer at matched parameters and generates text about twelve times quicker on the same CPU.

Open weights are not the whole story. Also on 30 September, the FTC opened an industry-wide investigation into Anthropic, OpenAI and the research group Metr, the first official US enforcement action on rogue AI agents, as The Guardian reported. OpenAI, meanwhile, is delaying its IPO and holding back a model release over safety concerns, per Ars Technica. The contrast is hard to miss: the models shipping without friction are the small ones, and the companies under scrutiny are the ones with the largest valuations.