Open-Weight Models

Model · Token classification

OpenMed-NER-BloodCancerDetect-TinyMed-65M

OpenMed

Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for…

Model

t5-v1_1-xxl

Google

Google's T5 Version 1.1 T5 Version 1.1 includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see here. - Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning. - Pre-trained on C4 only without mixing in the downstream tasks. - no parameter sharing between embedding and classifier layer - "xl" and "xxl" replace "3B" and "11B". The model shapes are a bit different - larger dmodel and smaller numheads and dff. Note: T5 Version 1.1 was only pre-trained on C4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a…

Model · Text ranking

qnli-electra-base

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task. For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrainedcross-encoders.html]. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library)

The pre-trained model is this one - facebook/hubert-large-ls960-ft The DUSHA dataset used can be found here Fine-tuned in Google Colab using Pro account with A100 GPU Freezed all layers exept projector, classifier and all 24 HubertEncoderLayerStableLayerNorm layers Used half of the train dataset - 2 epochs - train batch size = 8 - eval batch size = 8 - gradient accumulation steps = 4 - learning rate = 5e-5 without warm up and decay Achieved - accuracy = 0.86 - balanced = 0.76 - macro f1 score = 0.81 on test set, improving accucary and f1 score compared to dataset baseline

Model · Text to video

MiniMax-H3-Turbo-Lora

Larryvrh

A LoRA for MiniMax-H3 that renders joint video + synchronized stereo audio in as few as 4 sampling steps instead of the usual ~20 — a ~5× sampling speedup — and keeps getting better as you add steps. For most work, use minimaxh3turbov4step600ema.safetensors. It's the markedly better micro-detail (faces, fingers, fine texture), and the over-sharpening / plastic look of the earlier v1 (~850) line is fully resolved. v4 introduced a static-frame enhancement — a big win for static and small-motion content. The one trade-off shows up only at 4 steps with large, fast motion, where v4 can produce motion-smear / trailing ghosting (we're actively fixing this). Two things address it: - Use 6–8 steps.…

Model · Image to image

FLUX.2-small-decoder

Black Forest Labs

FLUX.2 Small Decoder is a distilled VAE decoder that serves as a drop-in replacement for the standard FLUX.2 decoder. It delivers faster decoding and lower VRAM usage with minimal to zero quality loss. The encoder remains unchanged. 1. ~1.4x faster decoding compared to the full decoder. 2. ~1.4x less VRAM at decode time, enabling higher resolutions without running out of memory. 3. ~28M decoder parameters (vs ~50M in the full decoder) thanks to narrower channel widths ([96, 192, 384, 384] vs [128, 256, 512, 512]). 4. Minimal quality loss — images are almost identical. 5. Available under the Apache 2.0 license. Compatible with all open FLUX.2 models: - This model is not intended or able to…

Model · Token classification

ner-german-large

Flair

This is the large 4-class NER model for German that ships with Flair. Based on document-level XLM-R embeddings and FLERT. So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington ging nach Washington". The following Flair script was used to train this model: Please cite the following paper when using this model. The Flair issue tracker is available here.

Model · Text to speech

kokoro-inno-clone-tuner

Jeremy Braun

Zero-shot voice tuner for Kokoro-82M. Outputs base Kokoro compatible voice packs @ [510, 1, 256]. Same passage for every voice, enrolled from the references. LibriTTS-R speakers are dev-clean held out from training. Integrated into Kokoro-FastAPI (v0.9.0+) The pack is a plain tensor; torch.save(pack, "voices/amme.pt") makes it a voice file like any other, prefixed by accent and gender like the stock packs. The pitch-tracking ceiling is set automatically from the reference's harmonic spacing, so band-limited or archival sources land in the right octave without tuning. - enroll(..., fmax=180) overrides it if a voice still reads the wrong register. Enrollment embeds an input audio sample via…

Model · Image classification

convnextv2_large.fcmae_ft_in22k_in1k_384

PyTorch Image Models

A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

Model · Image to video

MiniMax-H3-encoder-GGUF

Joey

GGUF quantizations of the Qwen3-VL-32B vision-language text encoder used by MiniMax-H3 in ComfyUI. The H3 DiT quants are here: joeygambino/MiniMax-H3-GGUF. You need one file from each repo to run H3 — the DiT alone will not generate anything. Load these encoders with H3 Clip Loader (Any) from not the stock CLIPLoaderGGUF node. The H3 text encoder is a truncated Qwen3-VL-32B - 50 layers, no final norm, no lmhead - and its vision tower ships separately as the -mmproj-F16.gguf sidecar. Stock ComfyUI-GGUF only merges an mmproj when the encoder's architecture is qwen2vl; Qwen3-VL reports qwen3vl, so the sidecar is never merged at all, and the resulting missing vision tensors surface as a…

Model · Image segmentation

segformer_b2_clothes

Mateusz Dziemian

SegFormer model fine-tuned on ATR dataset for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/humanparsingdataset". Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf" The license for this model can be found here.

Model · Token classification

OpenMed-NER-DiseaseDetect-BioMed-335M

OpenMed

Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.…

Model · Token classification

OpenMed-NER-OncologyDetect-MultiMed-568M

OpenMed

Specialized model for Cancer Genetics - Cancer-related genetic entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for cancer genetics - cancer-related genetic entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the…

Model · Token classification

OpenMed-NER-AnatomyDetect-ElectraMed-109M

OpenMed

Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for anatomical entity recognition - anatomical structures and body parts. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.…

Model · Image text to video

Minimax-H3-nvfp4-INT4-INT8-Convrot

Jay

This repository is a community-compiled collection of quantized and pruned weights for MiniMax H3 (Hailuo 3.0), optimized for local inference environments like ComfyUI. By unifying various quantization formats (INT4, INT8, Mixed, and NVFP4) into a single structured repository, this hub makes it easier for users with consumer GPUs (16GB - 24GB VRAM) to experiment with MiniMax H3's powerful omni-modal text/image/audio-to-video generation capabilities. If you are new to local generation and aren't sure what to download, use this guide based on your graphics card. Perfect for RTX 4070 Ti Super, RTX 4080, etc. Perfect for RTX 3090, RTX 4090, etc. Exclusively for RTX 5090, PRO 6000, and other…

Model · Fill mask

bert-base-japanese

Tohoku NLP

This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for the pretraining are available at cl-tohoku/bert-japanese. The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately…

Model · Question answering

bert-large-uncased-whole-word-masking-squad2

Deepset

This is a berta-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. Haystack is an AI orchestration framework to build customizable, production-ready LLM applications. You can use this model in Haystack to do extractive question answering on documents. To load and run the model with Haystack: For a complete example with an extractive question answering pipeline that scales over many documents, check out the corresponding Haystack tutorial. deepset is the company behind the production-ready open-source AI framework Haystack. We also have a Discord community open to everyone!

If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! distilbert-NER is the fine-tuned version of DistilBERT, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of Named Entity Recognition (NER). This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model…

Model · Text to image

revanimated

Stable Diffusion API

Get API key from ModelsLab, No Payment needed. Replace Key in below code, change modelid to "revanimated" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "", "modelid": "revanimated", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera"…

Model

mbart-large-50-one-to-many-mmt

AI at Meta

This model is a fine-tuned checkpoint of mBART-large-50. mbart-large-50-one-to-many-mmt is fine-tuned for multilingual machine translation. It was introduced in Multilingual Translation with Extensible Multilingual Pretraining and Finetuning paper. The model can translate English to other 49 languages mentioned below. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forcedbostokenid parameter to the generate method. See the model hub to look for more fine-tuned versions. Arabic (arAR), Czech (csCZ), German (deDE), English (enXX), Spanish (esXX), Estonian (etEE), Finnish…

Model · Any to any

gemma-4-E2B-it-MLX-4bit

LM Studio Community

LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord. 4-bit quantized version of gemma-4-E2B-it using MLX, optimized for Apple Silicon. Special thanks to the Apple Machine Learning Research team for creating MLX. LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful…

Model · Text to image

FLUX.1-schnell-gguf

City

This is a direct GGUF conversion of black-forest-labs/FLUX.1-schnell The model files can be used with the ComfyUI-GGUF custom node. Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions. Please refer to this chart for a basic overview of quantization types.

Model

mt5-large

Google

mT5 is pretrained on the mC4 corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish…

BERTimbau Base is a pretrained BERT model for Brazilian Portuguese that achieves state-of-the-art performances on three downstream NLP tasks: Named Entity Recognition, Sentence Textual Similarity and Recognizing Textual Entailment. It is available in two sizes: Base and Large. For further information or requests, please go to BERTimbau repository. If you use our work, please cite