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Open-Weight Models

An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.

Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.

11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships

Updated 2026-10-19 · How the library is built

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11,442 models, sorted by most downloaded.

Checkpoints for oriented-det (pip install oriented-det). Download by slug (not by filename): Repo: dl4eo/oriented-det-pretrained. Docs: pretrained/README.md. Finetune from 1×, not 3×. DOTA 3× is on Hub for AP75; leaky eval-val is train+val tile memorization. SSDD / HRSID / FAIR1M have no Hub zoo — finetune these DOTA 1× slugs locally. DOTA evalmap50 in the package manifest is leaky val tiles. Advertised DOTA mAP50 below is official Task 1. HRSC mAP50 is held-out ImageSets test. From v0.3.1, un-suffixed Rotated RetinaNet slugs are OBB (usehbbformatching: false). Circum-HBB lives at hbb. v0.3.0 unsuffixed filenames stay on this repo for old clients. Each slug has a content-addressed.pth plus…

Open weights apache-2.0 oriented-det
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Model · Text to speech

orpheus-3b-ptbr-lora

Lumazini Code

opedromartins/ASR-datasets-ptbr - freds0/TAGARELA basemodel: - canopylabs/orpheus-3b-0.1-ft pipelinetag: text-to-speech - text-to-speech - tts - orpheus - lora - unsloth - snac - portuguese - brazilian-portuguese - llama.cpp

Open weights apache-2.0
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Model · Text generation

orthomni-codeco

Huynh-Nhat Pham

Vietnamese spelling correction model: a BARTpho / MBart seq2seq model (~400M parameters), quantized to INT8 for inference on CPU. Two formats ship in this repository: CTranslate2 (ct2/) and ONNX Runtime (onnx/). This repository holds the model weights used by the Python package orthomni-codeco on PyPI. You normally don't download these files by hand: the package fetches the files of the backend you use on first run. Requires Python >= 3.9. PyTorch is not needed: the default runtime dependencies are only ctranslate2, numpy, sentencepiece and huggingface-hub. Create the model once and reuse it. Later runs load from the local cache and work offline. An async variant is also available: To use…

Open weights 1,024 tokens ctranslate2
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Model · Speech recognition

orukeet-r15-0100

Oruk

Submission withdrawn September 19, 2026 (UTC). Open ASR PR #221 is closed. The leaderboard submission has been withdrawn; the checkpoint, code, research results, and exposure disclosures remain available as historical research artifacts. This repository preserves the original R15-0100 Orukeet checkpoint originally proposed as an earlier checkpoint for Open ASR submission #221. The artifact is unchanged from the archived September 6, 2026 model. It has a FastConformer encoder and TDT decoder derived from NVIDIA Parakeet TDT 0.6B v3, with multilingual/accent adaptation and fitted temporal Gabor kernels materialized in the native weights. The NeMo file is orukeet-r15-0100.nemo (2,509,342,720…

Open weights cc-by-sa-4.0 nemo
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Model · Text generation

oss-jev-qwen05-decision

Gyan Bhambhani

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 2 - evalbatchsize: 8 - lrschedulertype: linear - trainingsteps: 2000 - Transformers 5.20.0 - Pytorch 2.14.1+cu130 - Tokenizers 0.23.3

Open weights apache-2.0 494M parameters 32,768 tokens transformers
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Checkpoints of a marginal RL run on ByteDance/Ouro-1.4B-Thinking (algorithm grpo) Tag 20261009-ragged-rl, Slurm job 154711, node orchard-flame-18, 145 optimizer steps. Each globalstepN/ is a complete native Hugging Face save written by marginalexperiments/train.py: FP32 model shards (backbone and earlyexitgate), tokenizer, config.json, policyconfig.json (cache rule, minimum depth, sampling settings), trainingstate.pt (AdamW optimizer state and run arguments) and the READY marker written last. No FSDP merge is needed: policyconfig.json records cacherule: fulldepthperloopv1: the policy was trained with ragged decoding, so evaluate it with the Dense-Ragged engine (sampled exits, minimum depth…

Open weights
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Object-hallucination probe trained on a LLaVA-1.5-13B COCO corpus, with three independent training seeds (0, 42, 1337). It is not interchangeable with the 7B release: features, geometry and thresholds are model-specific. 10,000 COCO train2014 images split 80/20 by image: 8,000 train / 54,253 object spans and 2,000 holdout / 13,789 object spans. Captions are generated by 13B itself, so this corpus is not the 7B corpus. Thresholds are selected by best F1 on the COCO validation split and then frozen. Nothing is tuned on the test holdout, AMBER, or POPE. COCO test holdout — mention view, n=13,787, 16.19% positive AMBER, strict zero-shot — n=2,367 rows / 977 images, 5.37% positive, COCO…

Open weights
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This model is a fine-tuned version of jeremierostan/Qwen3-1.7B-teacher-note-severity. It has been trained using TRL. This model was trained with SFT.

Open weights transformers
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Model · Text generation

outward-gemma3-270m

Harsh tuli

The on-device storyteller for Outward, an offline walking adventure. You go outside, find something real ("rainwater running down a drain"), and this model writes the next 60–120 words of the story around it. It runs entirely on the phone, in the browser, with llama.cpp (wllama). Modified by the Outward project. This is a Model Derivative of google/gemma-3-270m-it: fully fine-tuned on 3,186 stories written by a LoRA fine-tuned Qwen3.5-4B teacher (trained on Tinker), then converted to GGUF and quantized to Q40. The eval set has 50 held-out prompts × 3 samples, temperature 0.7, scored with the automatic rubric in the Outward repo: uses the discovery, 60–120 words, safe and outdoors (risky…

Open weights gemma 268M parameters 32,768 tokens gguf
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Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

Models that fit on one accelerator

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

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Open-Weight Models Explained

What is an open-weight model?

An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.

Is an open-weight model the same as open source?

Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.

Can I use an open-weight model commercially?

It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.

How much memory does an open-weight model need?

About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.

Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.