Open-Weight Models Under Apache License 2.0

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of google/gemma-4-E4B at revision 411aa17b749aa952df1359d2dcea73917a544d9a, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 95.1% (94.7% to 95.4%), against 96.9% (96.5% to 97.3%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

static quants of https://huggingface.co/Peter-A-P/smallprint-7b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

Extracts fifteen figures from the primary financial statements of a US SEC 10-K or 10-Q, as JSON. A LoRA fine-tune of allenai/Olmo-3-1025-7B at revision a81bae42db3975be1671e27b9c9a56da1a9f980f, trained on filings with their XBRL facts as the labels. No model graded another model anywhere in producing these numbers. 705 filings. On filings published before the base model's training cutoff it scores 96.0% (95.7% to 96.4%), against 97.4% (97.0% to 97.7%) after it. That difference is the contamination gap, reported and never used as the headline. Non-inferiority field by field, paired on the same filings, a three-point margin per field and Holm's adjustment across the fifteen, decided by the…

4-step SMD for video generation. The student adapters, standalone inference code and reproducible examples are included. Upstream base components are downloaded separately. Prompt: A small white delivery robot labeled "SMD" in large blue capital letters drives slowly along a bright hallway. The camera tracks alongside the robot, realistic motion, soft daylight. Use Linux, Python 3.12 and an NVIDIA GPU with CUDA support. The included inference code does not import the SMD training repository. The example was run on an A100 80 GB. Model CPU offload reduces GPU memory usage; enough host RAM is still needed to load the upstream components. Use --prompt, --seed and --output to generate another…

Model · Text generation

Smol-GPT-Fp8

Gggff123

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model · Text generation

Smol-GPT-RAW

Gggff123

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 4 - evalbatchsize: 8 - lrschedulertype: linear - trainingsteps: 1000 - Transformers 4.46.3 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.20.3

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Robotics

SmolVLA-Base-0.45B

Mithilesh Nakade

This repository contains weights or code derived from the SmolVLA foundational architecture developed by Hugging Face and the LeRobot Authors. This is SmolVLA-Base model cloned from Hugginface "lerobot/smolvlabase" repository. This was createed for ready-to-use custom model for easy inference during Hackathon challenge.

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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.

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

lerobot/smolvlabase를 SO-101 단일팔 픽앤플레이스 데이터셋 Masterji/dataset으로 파인튜닝한 SmolVLA 정책입니다. 이 저장소는 최종 체크포인트(step 10000)입니다. - 태스크 문장: Pick up the eclipse and place it on the box - 데이터: 30 에피소드 중 0~26번(27개)으로 학습, 27~29번(3개)은 held-out 평가용 - 관측: observation.state(6), 카메라 top·wrist(640×480) → 정책 입력 camera1·camera2로 매핑 - 액션: 6관절 위치(shoulderpan, shoulderlift, elbowflex, wristflex, wristroll, gripper) flow matching 손실이라 노이즈·시간 표본에 따라 값이 흔들립니다. 다른 모델의 손실과 직접 비교할 수 없습니다. eval loss가 가장 낮은 체크포인트와 최종 체크포인트를 같은 held-out 표본·같은 시드로 비교했습니다. 값은 액션 청크의 평균 절대 오차(관절 도, 그리퍼는 0~100 범위)입니다. flow matching의 eval loss와 실제로 샘플링한 액션의 오차는 순위가 다를 수 있어 둘을 함께 적습니다. 관절별 50스텝 평균 오차: shoulderpan.pos 4.39, shoulderlift.pos 8.90…

lerobot/smolvlabase를 SO-101 단일팔 픽앤플레이스 데이터셋 Masterji/dataset으로 파인튜닝한 SmolVLA 정책입니다. 이 저장소는 eval loss 최저 체크포인트(step 3500)입니다. - 태스크 문장: Pick up the eclipse and place it on the box - 데이터: 30 에피소드 중 0~26번(27개)으로 학습, 27~29번(3개)은 held-out 평가용 - 관측: observation.state(6), 카메라 top·wrist(640×480) → 정책 입력 camera1·camera2로 매핑 - 액션: 6관절 위치(shoulderpan, shoulderlift, elbowflex, wristflex, wristroll, gripper) flow matching 손실이라 노이즈·시간 표본에 따라 값이 흔들립니다. 다른 모델의 손실과 직접 비교할 수 없습니다. eval loss가 가장 낮은 체크포인트와 최종 체크포인트를 같은 held-out 표본·같은 시드로 비교했습니다. 값은 액션 청크의 평균 절대 오차(관절 도, 그리퍼는 0~100 범위)입니다. flow matching의 eval loss와 실제로 샘플링한 액션의 오차는 순위가 다를 수 있어 둘을 함께 적습니다. 관절별 50스텝 평균 오차: shoulderpan.pos 4.70, shoulderlift.pos…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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.

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.

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

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 · Robotics

smolvla_water_libero-1

Jose Edgar Hernandez Cancino Estrada

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Robotics

smolvla_water_libero-2

Jose Edgar Hernandez Cancino Estrada

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Robotics

smolvla_water_libero-4s-dart-full-anchor-xl-sd-rel-b64-dagger1

Jose Edgar Hernandez Cancino Estrada

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Visual question answering

SnapVL

akesh Kumar

1. Pre-Convergence Checkpoint: The metrics reported in this document were evaluated on an intermediate experimental checkpoint where training was paused early to analyze representation dynamics. These numbers do not represent fully converged final weights. 2. Closed-Schema Evaluation Protocol: Unlike generative vision-language models that decode answers token-by-token from an open vocabulary, SnapVL operates as a closed-schema decision classifier. Questions are framed either as Boolean verification (True/False) or multiple-choice candidate selection (N-choice). Direct numerical comparison with generative LLM decoders should take this protocol difference into account. 3. Scope: SnapVL is…

Snowflake/snowflake-arctic-embed-l-v2.0 converted to GGUF with llama.cpp b10630. Embeddings have 1024 dimensions. With Shabti, in custommodels.ini

GR00T N1.7 is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot groot 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…

底座里的 VAE、文本编码器与 Wan2.2-TI2V-5B 扩散主干仍从 Dexmal/DW05-Robotwin 取，本仓只放微调后的权重。 - 起点：Dexmal/DW05-Robotwin，剔掉与双臂机器人绑定的动作头与本体编码器（宽度对不上 SO-101，重新学） - 全量微调 + DeepSpeed ZeRO-1，每卡 batch 1 × 3 卡，20000 步，学习率 1e-4 cosine - SO-101 的 6 维状态填进 RobotWin 的 16 槽排布：前五个槽放五个臂关节，夹爪落在第 7 槽；三路视图用 顶视 + 腕部 + 腕部 - 本体状态在训练时没有归一化：推理时把 6 维状态排进 16 槽、补一位终止标志 0 后原样送入，不要套 RobotWin 的分位数归一化。 用法与评测脚本见 dexbotic 的 docs/so101.md（script/so101/dw05simcheck.py）， Apache-2.0，随底座 Dexmal/DW05-Robotwin。

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…

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. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Robotics

so101_vials_dr_randomized_grasp_sim250_GR00T17_real10_ft5000_20261008

Lior Ben Horin

Independent further-training of the new sim-only 20,000-step policy on 10 real10 episodes for 5,000 additional steps. Both real10 and Cosmos10 branches initialize from the same sim-only parent; neither continues from the other branch. - Native parent processors and statistics retained, with stock clipping; no unclipping ablation. - Visual tuning enabled, language tuning disabled, BF16 autocast with FP32 parameters; image augmentation enabled. - Checkpoints at 2,500 and 5,000 additional steps. This repository contains the final 5,000-step continuation. The native policy and processor files must remain together. See training-provenance.json for parent weight hash and exact provenance.…

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 LeRobot 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 walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

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 LeRobot 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 walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

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 LeRobot 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 walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

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 LeRobot 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 walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

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 LeRobot 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 walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short…

Model · Robotics

soarm_amazing_hand_act

Juxi Technology

An ACT (Action Chunking Transformer) imitation-learning policy trained on an SO-ARM101 follower arm equipped with an AmazingHand dexterous hand, performing a cube pick-up task. - 20 teleoperated demonstrations, 18,538 frames, 30 fps - The cube was recorded at 4 different table positions, 5 episodes each, to cover positional variation shoulderpan.pos, shoulderlift.pos, elbowflex.pos, wristflex.pos, wristroll.pos, gripper.pos Requires an environment matching this project (a customized lerobot that includes the soamazinghand robot definition): 1. --task must match the training singletask string exactly 2. Camera names and index order must match training 3. The robot must already be calibrated…

Model · Text classification

Solomon

Doccy Health

A LoRA adapter and trained answer heads for Qwen/Qwen3.8-27B that turn a document plus a set of structured questions into one probability per decision, each with a retrieval pointer to where the support for it plausibly sits in the source. It does not generate text. Give it a document once and ask structured questions against it. Each answer comes back as a probability, and with source spans beside it. The answer is read from the whole document; the spans are a separate word-overlap search over the source text. They are a pointer to where the support probably is, not the passage the answer came from — Evidence spans, below, measures the difference. There is no chat, no reasoning trace and…

Model checkpoints for the paper Self-Play Pretraining with Zero Data. Two randomly initialized transformers are trained in tandem: a generator proposes programs for a minimal universal Turing machine, and a learner is trained by next-token prediction on the executed byte sequences. No natural data is used at any point during training. These checkpoints are the learners from that process, released so that every result in the paper can be recomputed from the weights. All models are byte-level (vocabulary 256) decoder-only Llama-style transformers with a 4096-token context. The main self-play ladder, six model sizes. Learner weights are saved every 256 self-play rounds; sizes refer to…

Model · Robotics

SOMA-X

NVIDIA

SOMA (Unifying Parametric Human Body Models) is a unified framework that decouples identity representation from pose parameterization by mapping supported parametric models to canonical body and hand topologies and skeletons, enabling shared Linear Blend Skinning (LBS) pipelines across backends. The full-body layer supports six identity backends (SOMA-shape, SMPL, SMPL-X, MHR, ANNY, and GarmentMeasurements). SOMA-X v0.3 also includes wrist-local left/right hand layers with native SOMA identity and articulation priors plus interoperability with user-supplied MANO models. This model is ready for commercial use. SOMA is released under the Global SOMA is intended for use by computer vision…

An int8, ONNX version of facebook/wav2vec2-lv-60-espeak-cv-ft, a wav2vec 2.0 model that outputs espeak IPA phoneme labels (not words). It is the file the speech service of Sonari, an English course for Vietnamese learners, runs on CPU to score pronunciation: CTC forced alignment of the learner's recording against the expected phonemes, then a goodness-of-pronunciation (GOP) score per phoneme. It is not used to transcribe speech. Only the weights' format and precision changed. No training or fine-tuning was done. Use the commit-pinned URL above, not main: Sonari pins this exact file by SHA-256 and refuses any other, because its thresholds are calibrated on it. Later commits to this…

This repository hosts the 035 / 200k weights and a separate Rockchip build sysroot. Built on GR00T-WholeBodyControl / SONIC. Thanks to the GEAR team for their amazing and solid work, comprehensive open-source release and thorough documentation. The 035 step-200,000 weights (PT, ONNX and RKNN) and accompanying release configurations are licensed under the Apache License 2.0. Third-party software in the sysroot retains its own license terms. Real-robot motions can fail or cause falls. Validate in MuJoCo first, retain a safety harness / gantry on hardware, and protect all people nearby. Use the source repository downloader to select PT, ONNX or RKNN artifacts. It also fetches the root…

The Sovereign-Swarm-Coherence-v1-alpha repository contains the lightweight ONNX & WebAssembly (WASM) client-side package for running inference directly inside web browsers and mobile apps with 0ms server latency and zero cloud API infrastructure costs. - 100% Offline Capable: Zero network dependencies after initial WASM module download. Apache 2.0 License © ItsNotAI LABS

Model · Image and text to text

span-02

He Li

span-02 is a decision model: it answers typed questions about a state (JSON or text, plus up to 4 images) with a full probability distribution over the options, in the Decision Index /v1/systemone wire format. It is a fine-tune of Gemma 4 26B-A4B (mixture of experts, about 4B active parameters per token), quantized (NVFP4 experts, FP8 dense layers) and served by a custom Rust/CUDA engine for Blackwell GPUs (sm120: RTX 5090, RTX PRO 6000) that is included in this repo (engine/). Before answering it may think. If the top probability of the no-think answer is below 0.8, the model writes a greedy thought. Its length limit comes from a deterministic thought budget: a fixed cost model…

Model · Audio to audio

spansynth-edit

Sungkyun Chang

Synthesise music from MIDI, or add, remove, and modify notes in a recording by revising its MIDI. SpanSynth-Edit generates the selected region, using surrounding audio for timbre guidance. Try it in your browser: upload audio, transcribe with YourMT3+, edit the piano roll, and generate. See the local web setup to run the app yourself. - CPU inference is also supported. Measured peak VRAM was about 3.9 GiB on a GH200 for synthesis and both editing methods with default settings (20.48 s crop, 16 steps, CFG 2.0). Install PyTorch for your GPU first, then install the CLI without downloading the demo audio: Model and codec weights download automatically on first use. No token is required.…

Model · Text generation

spark-darwin-17b-3way

Charlie

Darwin uniform-soup of 3 Spark2.5-1.7B donors (226/226 tensors verified). Agentic/tool-use + uncensored + multilingual blend. Distillation donor for the 4B program. Needs transformers==5.17.0 + trustremotecode=True.