Open-Weight Models

SlimSAM (nielsr/slimsam-77-uniform) in ONNX for transformers.js, like Xenova/slimsam-77-uniform — whose image encoder files are reused here unchanged — but with a prompt encoder / mask decoder that takes box prompts. Xenova/slimsam-77-uniform's decoder declares no inputboxes, and transformers.js drops inputs a session does not declare, so a box passed to it is silently ignored. This decoder follows Meta's own SAM ONNX convention instead: a box is two extra points in inputpoints, labelled 2 (top-left corner) and 3 (bottom-right corner). Inputs and outputs keep transformers.js's names, so SamModel / SamProcessor drive it unchanged. One rule the caller must follow: a prompt without a box…

The six-H200 full-corpus run is in progress. Its rolling latest checkpoint is in a mutable Storage Bucket, with the verified step recorded in latest.json. The final weights will be published in this model repository. Live training and held-out validation metrics are updated throughout the run. The model initializes from OpenWAM-α at revision 52df4e66c82c5c8b480adcc8d01f4db7415dfb56. Its Slither adapter replaces the 80-dimensional robot action head with three outputs: cosine and sine of estimated mouse angle, and binary boost. It removes robot proprioception and uses fixed empty-prompt conditioning. Each example uses 33 frames at 30 Hz and 32 transition actions; the nine video frames are…

1,427,969,472 trainable parameters, initialized from pinned Wan2.1-T2V-1.3B. Frozen native video codec, 512×288 at 15 Hz, three seconds observed history, 30 Hz proxy angle/boost controls. Joint stochastic future blocks with native sigma flow matching. Policy fitting is a later separate stage. Resumable latest and best checkpoints: https://huggingface.co/buckets/mundoamundo/slither-wan-v3-20261001. Read latest.json or best.json for verified file pointers, sizes, and SHA-256. Two rotating slots per kind; no repeated weight commits to this Git repository. Checkpoints include model, optimizer, data cursor, and per-rank RNG. Dataset: mundoamundo/slither-wam-video-actions at…

Model · Text generation

slm-125m-base

Tijani Ohiokpehai

tohio/slm-125m-base is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage…

Model · Text generation

slm-125m-dpo

Tijani Ohiokpehai

tohio/slm-125m-dpo is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage…

Model · Text generation

slm-125m-sft

Tijani Ohiokpehai

tohio/slm-125m-sft is a small language model (125M parameters) built from scratch and aligned end-to-end using the slm-gpt engine. - Decoder-Only Transformer with Rotary Position Embeddings (RoPE) The base model was pre-trained across an interleaved multi-source domain mixture composed of: - FineWeb-Edu - DCLM-Edu - The Stack-Edu - NuminaMath-CoT - OpenMathReasoning - SLM-Synthetic-Pretrain 1. Pre-training: Multi-GPU Distributed Data Parallel (DDP) over rank-disjoint memory-mapped token shards. 2. Supervised Fine-Tuning (SFT): Full parameter instruction-tuning on ChatML formatted dialogues with prompt loss masking (ignoreindex=-100). 3. Direct Preference Optimization (DPO): Single-stage…

These are weight-only FP8 kernels for NVIDIA Volta (V100, SM70). They serve checkpoints whose weights are E4M3 with one power-of-two (UE8M0) scale per 32 × 32 block, the dense format of DeepSeek-V4.1-Flash (weightblocksize: [32, 32], scalefmt: ue8m0). Volta has no FP8 tensor cores, so the weights stay FP8 in memory and are expanded to FP16 inside the kernels. Weight memory falls from 2 bytes per parameter (FP16 dequantization) to 1.0625 bytes. For DeepSeek-V4.1-Flash's per-layer dense set at TP4, decode-size products (1–16 rows) run 9–36% faster than FP16 weights with cuBLAS. Individual projections vary (see Results). This repository is a distribution of a patch series for 1Cat-vLLM, 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 google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, 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 95.2% (94.7% to 95.5%), 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…

static quants of https://huggingface.co/Peter-A-P/smallprint-2b 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 google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, 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 95.2% (94.7% to 95.5%), 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 google/gemma-4-E2B at revision d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f, 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 95.2% (94.7% to 95.5%), 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 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 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 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…

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…

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…

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…