Open-Weight Models

Model · Tabular classification

mitra-classifier-1.1

Autogluon

Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model - it just wraps around the class. This model has been fine-tuned on the model: sentence-transformers/all-mpnet-base-v2. It is pretrained for the language: en. To use this model using sentence-transformers: To use this model for clustering with LinkTransformer installed: We evaluate using the standard LinkTransformer information retrieval metrics. Our test set evaluations are available here. The model was trained with the parameters: torch.utils.data.dataloader.DataLoader of length 31 with parameters: linktransformer.modifiedsbert.losses.SupConLosswandb If you use our…

Predicts which MBTA commuter rail track/platform a train will use, using a small tabular neural-network ensemble trained on historical assignments. This card documents the artifacts in output/ensemble20250906124755. - trackpredictionensemblemodel0final.keras … trackpredictionensemblemodel5final.keras — individual ensemble members - trackpredictionensemblemodelbest.keras — best checkpoints during training (may match final) - trainingreport.md — training configuration and metrics Note: Ensemble training currently does not emit a vocab.json. See “Preprocessing & Vocab” below. Models expect integer indices for stationid and routeid, and raw directionid 0/1. In training, indices are produced by…

Model · Tabular classification

rt-plurel

Stanford Tabular and Relational (STAR) Project

RT-PluRel is a Relational Transformer checkpoint pair for in-context / few-shot entity prediction over multi-table relational databases (no per-task gradient training). Same architecture and file layout as stanford-star/rt-j — drop-in replacement. Architecture: ~85.6M blocks, dmodel 512, 8 heads, dff 2048 · text columns embedded with all-MiniLM-L12-v2 (dtext 384). Each folder contains model.safetensors (weights) and config.json (dims + text-embedding model). The paper/ subdirectory preserves the earlier RT-PluRel release:.pt checkpoints (12 blocks, dmodel 256, dff 1024) pretrained on synthetic relational databases generated by PluRel, plus the continued-pretraining and fine-tuned RelBench…

Model

codeparrot-small-multi

Tullus Augustus

A few weeks ago i made a mini gradio server that was special built for running a multimodal. So now i made it load the new artifac, and more INFOS Below ⬇ CodeParrot-Multi is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript". You can load the CodeParrot-Multi model and tokenizer directly in transformers: or with a pipeline: The model was trained on the small Github code small after near deduplication, a subset of Github code dataset with the following settings: The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens. We evaluated…

Model · Tabular classification

wine-quality

Vova Manannikov

No red wine was drunk (unfortunately) while training this model

Model · Text classification

electra-base-discriminator-nvidia-aegis-v2-augmented

Leonardo Mauro

This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 50 - numepochs: 10 - Transformers 5.2.0 - Pytorch 2.10.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2

CTranslate2 conversion of efwkjn/whisper-ja-760M. For usage instructions follow Systran/faster-whisper-large-v3. Due to vocab changes ctranslate2>=4.7.1 required for faster-whisper. See original model for more details.

Model

RINIQ-MERNIK-GGUF

Wepiqxx

RINIQ is a new finetune built by layer surgery, quantized by the MERNIK engine. Three Qwen3.5-9B donors, one 427-tensor trunk (Ornith's MTP head excluded), per-block donor maps — no averaging of divergent blocks, ever. (full ledger in FUSION.md there). - Ox = OxCoder-9B (agentic code distillate, HE 90.24%) — base + globals - Neo = NeoHorse-1-9B (thinking model) — donor of blk 31 - Orn = Ornith-1.5-9B-MTP trunk (GDN hybrid) — donor of mid/late blocks Recipes: M1 = Ox + Orn blks 15,19,23,27 (weight compass) + Neo blk 31. M2 = Ox + Orn blks 24,25,26 (imatrix compass) + Neo blk 31. M3 = M1 + blks 0–8 weight-averaged Ox+Neo (soup backbone) — soup buried (−4.9pp vs M2, honest scar in FUSION.md).…

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

baidu/Unlimited-OCR as one repository max serve opens directly on Apple Silicon: baidu's weights unchanged (model.safetensors, byte-identical to upstream at revision 07dea832e22aefee32ad281d4b80551282e1c168, sha256 2bc48a7a110061ea58fff65d3169367eebe3aee371ca6968dc2219c1b2855fc6), the tokenizer files as published, and the checkpoint's config.json with two keys removed (automap, modeltype) so MAX can load it without trustremotecode. The runtime is a MAX custom architecture with two Mojo custom ops, served from https://github.com/kthr/unlimited-ocr-max as an OpenAI-compatible endpoint on the Metal GPU or the CPU. The repository also carries model-int8.safetensors, this port's weight-only int8…

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

Hades 8B is an open-source Small Language Model built on Meta's Llama 3.1 8B architecture. Post-trained across 50,000 steps on C-DAC Airawat supercomputing infrastructure using the mlabonne/FineTome-100k dataset, it is optimized for high-reasoning instruction following, structured output, and fast local execution. Evaluated using lm-evaluation-harness (lm-eval) in Zero-Shot Chain-of-Thought mode: ollama run hf.co/ProjectMosiacAI/Hades-8B

Model · Image and text to text

Qwen3.8-27B-Continuum-mxfp4-mlx

Gheorghe Chesler

(He slides three glasses across the bar—wine for Shakespeare, water for Data, and a mysterious blue liquid for Spock) This model is a merge of: - nightmedia/Qwen3.8-27B-Brainwaves - migtissera/Synthia-4-27B Brainwaves nightmedia/Qwen3.8-27B-Brainwaves migtissera/Synthia-4-27B Late-Night Architectural Takeaways The Quantization Shield (mxfp8 Master Pass): Hitting 0.735 ARC-C on the 8-bit layout proves that your Cold-Fusion flagship anchors and Migel Tissera's Synthia agent paths reached absolute geometric equilibrium. Instead of losing performance to the 0.596 Heretic collapse, the curved hypersphere calculation completely shielded the model's core intelligence. The Perplexity Sweet Spot…

An Anima fine-tune by silvermoong, using the "Evolved" training strategy with a focus on stable hands, a neutral art style that works with artist tags, and multiple characters. Update ComfyUI or Forge Neo to the latest version and restart before loading either 2.9B model. Older versions may not load or run the model correctly. Each checkpoint is a 5.84 GB BF16 diffusion model. Both use the same separate text encoder and VAE listed below. SHA-256 checksums are in releases.json. Recommended settings for standard and Turbo versions, including 2.9B, are listed below. See the Civitai release page for sample images. These are BF16 diffusion-model checkpoints. The text encoder and VAE are separate…

lerobot/smolvlabase fine-tuned on Prashant-77/thaliall (1050 scripted-expert episodes, 7 skills, language-conditioned, 3 cameras). Camera keys are renamed at train and inference time: overhead → camera1, wrista → camera2, wristb → camera3 (--renamemap; runtime/executors.py applies the same map). Checkpoints in this repo. Root = 20 000 total steps. step14000/ = the best per-skill checkpoint (14 000 steps at batch 16 on a T4; the last 6 000 steps ran at batch 4 with a fresh optimizer on a smaller GPU and lost ground). Policy-only success per skill from task-consistent start states, 20 held-out seeds (eval/skilleval.py --kind smolvla): The scripted expert reaches 9/10 on the full task; the…

Model

kinyarwanda-mms-vits-finetune

BANA Emmy Tresor

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Custom TabTransformer-style PyTorch model for churn prediction, trained on a synthetic ShopSphere e-commerce dataset for an academic Data-Driven Marketing Analytics project. covering Historical / Update / Post-Update periods. Selected on the validation set by maximizing expected net value under the assumption that a missed churner costs 25x more than an unnecessary retention outreach (₹5,000 lost value vs. ₹200 intervention cost) — not by F1. The dataset is synthetic and does not represent real ShopSphere customers. Results, including the baseline comparison, should not be interpreted as causal evidence that the platform algorithm update caused churn, and the hyperparameter search covered a…

Model

GLM-5.3-Flash-NVFP4-Wabi

Tenhkspark

Versions below for what a v2 would have to clear. A derivative of the official nvidia/GLM-5.3-Flash-NVFP4 checkpoint in which the layers NVIDIA's release left in BF16 — the attention linears (KDA fused inproj/out projections, MLA q/kv/o, indexer wqb), the shared experts (gate/up/down), and lmhead — are quantized to W4A16 NVFP4 (weight-only 4-bit, group size 16). Routers, norms, embeddings, the vision tower, and the MTP layer (layers.45) stay BF16; the routed experts keep the stock W4A4 NVFP4 quantization. enjoying the incomplete This checkpoint does not boot on the stock vLLM image. It needs the kda-quant and mla-quant source overlays from the companion repository (plus glm5next-mtp-bf16…

Binary classification model that predicts whether EUR/USD will close higher (UP) or lower (DOWN) the next trading day. The model uses 53 features including: - Williams %R, CCI: Additional momentum indicators See predict.py for a complete inference example. Based on published financial ML literature: This model is for research and educational purposes only. It is NOT financial advice. Forex trading involves significant risk. Past performance does not guarantee future results. Realistic accuracy for daily direction prediction is 52-56% (literature consensus). This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Model · Tabular classification

imdb

Devang Parekh

Problem type: Multi-class Classification; Model ID: 1530155186; CO2 Emissions (in grams): 0.0186; Loss: 0.694; Accuracy: 0.487; Macro F1: 0.218.

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.20.0

LoRA Flux.1-dev pour le personnage Emma Vaganova, entraînée avec le token 3mm@. Téléchargement: onglet Files and versions de ce dépôt. Utiliser 3mm@ dans le prompt (souvent en fin de phrase: Style of 3mm@). - Résolution: 896×1024 ou buckets proches (aligné sur l’entraînement multi-résolution). Les dossiers d’état d’entraînement (-state/) ne sont pas publiés ici pour l’instant: reprise Comfy = usage local uniquement. Personnage fictif inspiré de références photo; usage responsable et conforme aux conditions FLUX / OpenRAIL. Voir MODELCARD.md (steps, reprise, hyperparamètres, SHA256, limites connues).