Open-Weight Models

Stage-1 tokenizers and stage-2 paired-pretraining checkpoints for fMRI-LM, a foundation model that aligns functional MRI with language. Three variants, differing in the objective the stage-1 fMRI tokenizer was trained with. vq-contrastive/ — vector quantization + SigLIP contrastive alignment. vq-domain/ — vector quantization + adversarial domain loss. mae/ — masked autoencoding (mask ratio 0.5) + adversarial domain loss. All three were trained on UK Biobank with robust normalisation and Qwen3-0.6B. Stage-2 files are DeepSpeed checkpoints already merged to a single file. The MAE stage-1 file loads with MaskedAutoencoderViT; the two VQ stage-1 files load with the Tokenizer class. They are not…

Checkpoint collection for four real-world tasks and four model families. - joint is a top-level folder alongside the four tasks, for models trained on all tasks. - Task-specific runs sit directly under / /. Each run includes its original checkpoint format and inference/training metadata. Reserved folders contain a README; inspect run folders for available weights. See CHECKPOINTUPLOADS.md for the upload contract and commands, checkpointlayout.json for the path schema, and hfcheckpointimports.json for pinned source revisions and file hashes. Imported run READMEs retain their original training and evaluation limitations; these historical runs are not asserted to use the current benchmark…

Model

fiddle

Computational Metabolomics Research Group

Yuhui Hong and FIDDLE contributors. FIDDLE v2.0.0 Orbitrap prediction and rescoring checkpoints. Copyright 2024 Yuhui Hong. Upstream release. Apache-2.0 follows the release's project licence; no separate checkpoint-specific licence statement accompanies the archives. The unchanged upstream licence notice is included as 2.0.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Browser bundle of a chess-playing fruit-fly connectome — the complete FlyWire FAFB v783 brain (138,639 neurons, 15,091,983 synapses, wiring and excitatory/inhibitory signs frozen) with a learned encoder, one positive gain per synapse, per-neuron homeostasis, and a decoder, trained on Stockfish-annotated Lichess positions. Recovers Stockfish's top move 28.3% of the time on held-out positions (ChessFly, the original recipe by Maxime Labonne: 30.4%). Play it: https://flymate.naklitechie.com · code: https://github.com/NakliTechie/flymate Files are raw little-endian arrays consumed by the page's WebGPU kernels; meta.json carries shapes, the 1968-move vocabulary and torch reference outputs for…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Model

g1_depth_dodge

Martino Russi

Frozen SONIC whole-body controller + seven rank-16 LoRAs on the decoder, conditioned on a 128-d percept from a head depth camera (Theia-Tiny tokens + temporal adapter). PPO, iteration 4000. Consumed by lerobot's DepthDodgeController for the Unitree G1. Camera contract: 64x96 optical-Z depth in metres, 45 deg vertical FOV, 0 deg pitch, valid 0.2-6 m, 25 Hz. See manifest.json for the full training config.

Model · Reinforcement learning

ganglion-haltere-cursor

Artem Skulimovskiy

Two checkpoints of the Haltere fly-brain connectome (a 30,000-neuron recurrent network with the connectome's structure and signs, flight-trained) with a linear motor readout that turns the network's motor-neuron rates into a cursor or view velocity, trained by imitation of a proportional controller in Ganglion, the reflex layer that runs them against live applications at 100 Hz. The report-.json files beside them are the training and suite reports they were selected from. Everything here was measured on one machine (RTX 4090, Windows 11); the numbers are the suite's and the live harness's, with their caveats, and are documented in full in the repository's TRAINING.md and HALFLIFE.md. The…

A hidden Markov model distilled from mhdang/gelatwo-common-gen-gpt2-large, used as the tractable proposal for keyword-constrained generation on CommonGen in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). Locally constrained decoding (LCD) enforces a constraint by masking next tokens one step at a time, which biases the samples it draws. P-GCD instead combines this HMM with a tensorized finite automaton to form a proposal that carries both the logical and the probabilistic information about the target distribution, and samples from it with sequential Monte Carlo. The HMM supplies the lookahead the This is not a fine-tune of the base LM and shares…

gpt2-large fine-tuned on CommonGen, used as the base language model for the CommonGen experiments in Mitigating Bias in Locally Constrained Decoding via Tractable Proposals (arXiv:2606.01926). This is a plain causal language model: it supplies the base distribution that GCD and P-GCD steer. The tractable proposal it is paired with is the HMM at which shares its 50257-token vocabulary. configs/common-gen.yaml in github.com/MhDang/gelatwo already points at this checkpoint, so the CommonGen runs need no override.