Open-Weight Models

This lane contains 0 distinct original checkpoint variants. Each checkpoint uses a content-addressed path and an asset manifest preserving its schema, decoder role, available format/profile binding, codec, source declarations, and companion corpus or embedding assets. Unknown bindings remain unknown. Registration and publication do not qualify numerical quality, source-only reconstruction, teacher use, proof authority, or serving. No retained checkpoint is activated for inference. The Legal 768D checkpoint is inherited initialization only; Codebase, Intent, and Security 768D lanes have no retained checkpoints. Codebase 384D envelopes retain their borrowed Security payload identity and are…

This lane contains 2 distinct original checkpoint variants. Each checkpoint uses a content-addressed path and an asset manifest preserving its schema, decoder role, available format/profile binding, codec, source declarations, and companion corpus or embedding assets. Unknown bindings remain unknown. Registration and publication do not qualify numerical quality, source-only reconstruction, teacher use, proof authority, or serving. No retained checkpoint is activated for inference. The Legal 768D checkpoint is inherited initialization only; Codebase, Intent, and Security 768D lanes have no retained checkpoints. Codebase 384D envelopes retain their borrowed Security payload identity and are…

Model

seismic-interpolation-benchmark

GeoBrain

Deep-learning-based missing-trace reconstruction on pre-stack seismic shot gathers. Given a shot gather with masked (missing) traces, the model reconstructs the full gather. Each experiment is one architecture trained on one missing-trace scenario with one random seed. Model directories are suffixed with the dataset the model was trained on: - mobil — Mobil field dataset (pre-stack seismic shot gathers) - segc3 — SEG C3 synthetic dataset (wiki.seg.org/wiki/C3): 9 regular shots, 201 traces x 625 time samples, dt = 2 ms - Chai2020 UNet (chai2020unet) — 2D U-Net (Chai et al., 2020, IEEE TGRS, DOI 10.1109/TGRS.2019.2961015): 50 layers = 19 convolutions (5x5, same padding) + 18 ReLU + 4 max-pool…

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. 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.

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. 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.

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. 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.

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.

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.

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.

Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. 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.

Anonymized checkpoint ladders for a study of safety behavior under RLVR self-play and SFT. Folders are codenames; the codename->setting mapping is withheld. (actor/modelworldsize.pt + config); use veRL modelmerger/convert2hf to load. step180 is HF safetensors.

Model

self-supervised

Joaquín A. González

A structured set of research notes on Self Supervised, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw…

Model · Sentence similarity

semantic-lite-2

Zulfah

Semantic-Lite-2 is a lightweight multilingual sentence embedding model that produces 256-dimensional semantic vectors. It is designed for semantic search, sentence similarity, clustering, retrieval, and retrieval-augmented generation (RAG) tasks. The model is built on top of the Spark-X2.5-1.7B backbone using a frozen-backbone plus trainable-projection-head approach. The 256-dimensional output keeps vector storage compact while preserving strong retrieval quality. Vectors are L2-normalized, so cosine similarity is computed as a simple dot product. Evaluated on 500 Indonesian NLI evaluation pairs (retrieval task, chance level 0.2%): Cross-lingual evaluation (10 languages, 20 pairs per…

Model

semg-intent-restoration-pipeline

Jorge Ortiz Ceballos

Three chained PyTorch models (plus one exploratory variant) implementing the Each 200 ms / 400-sample sEMG window (2000 Hz, single channel, MyoWare-compatible) is routed through: intent detection → quality assessment → (optional) restoration. Operating thresholds (τintent = 0.40, τquality = 0.50) were selected via grid search on the validation split only, and evaluated once on a held-out test split. If you want to reproduce the pipeline exactly as described in Section 4.7 and Figure 1 of the paper, use only the first three files. The task-aware variant is a separate experiment reported transparently as a limitation, not a replacement for restorerbest.pt — see the "Downstream Motor-Intent…

Model · Text generation

semsimula-ladder-live-owt-d384-l2-attention-potential-rfqk-norc

Dimitar Gueorguiev

Settled is the mean of the last three evals (54.20, 54.39, 54.22); best 52.50 at step 31,000, final 54.22. Consecutive evals near the end differ by about ±1 PPL. - The field alone is a real but weaker route. It lowers the conservative-only model's perplexity by 6.0%; the register path alone lowers it by 8.0%. - Beside the registers it adds almost nothing: G3′ is 0.4% better than G2. The two routes largely substitute for each other. This model sees exactly 532,480,000 training tokens — 32,500 steps × 32 × 512 = 532,480,000 tokens — the same budget, corpus, tokenizer and validation batches as every other model in the table. No model is trained longer than another. - The base is F3.1: the…

Model · Text generation

semsimula-ladder-owt-d384-l2-attention

Dimitar Gueorguiev

One arm of the SemSimula mechanism ladder — OpenWebText, d=384, L=2: five models that differ by exactly one mechanism each, so that every gap in the ordering prices one component by construction rather than by attribution. Italic entries are pre-registered predictions, recorded before the run. The ordering is itself the prediction; any inversion is a result. Every arm above sees exactly 532,480,000 training tokens — 32,500 steps × 32 × 512 = 532,480,000 tokens — on the same corpus, tokenizer and validation batches. No arm is trained longer than another, so a gap in the table is a gap between mechanisms and never a difference in budget. The models below integrate a damped mechanical system…