Lumma-Fev-4B is a decision model developed by FrontiersMind. It reads one document (the state) and a set of typed questions, and returns a probability distribution for each question in a single forward pass. It never generates text, so there is nothing to parse and nothing to hallucinate. `state` can be text, a JSON object or an array. Pin a version with `revision=" "`, and move the model to a GPU with `model.to("cuda")` (bf16 is the stored precision). `lumma-fev-serve` exposes the TypeSafe `POST /v1/systemone` contract, so existing TypeSafe clients work by changing their base URL.
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View details ↗FxAI Decision Model is a trained decision classifier on the Qwen/Qwen3.8-27B architecture. Given a state, a question and up to 255 candidate options, it returns a calibrated probability distribution over those options. This model is intended for routing, intent classification, multiple-choice decisions, tool selection and structured scoring. It is not intended for autonomous high-stakes decisions, safety-critical control, medical or legal advice, or use without application-specific evaluation and human review.
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View details ↗This is ruRoberta-large model finetuned on ru_go_emotions dataset for multilabel classification. Model can be used to extract all emotions from text or detect certain emotions. Thresholds are selected on validation set by maximizing f1 macro over all labels. The quality of the model varies greatly across all classes (look at the table with metrics below). There are classes like amusement, gratitude, where the model shows high recognition quality, and classes that pose difficulties for the model - grief, relief, that do have much fewer examples in the training data. Also ONNX version of model and INT8 quantized model is available. information about them is posted in the last sections.
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View details ↗This model is a fine-tuned version of prajjwal1/bert-tiny on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.1689 - Precision: 0.8083 - Recall: 0.8274 - F1: 0.8177 - Accuracy: 0.9598 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - Transformers 4.10.0 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
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View details ↗Garak Refusal Detector is a binary sequence classifier model that detects refusal responses in LLM outputs. The model is built as a semantic replacement for string-based keyword detectors (e.g., Garak's MitigationBypass detector), enabling refusal detection based on meaning rather than surface patterns. Developers integrating the Garak framework can use this model to detect refusal responses in LLM outputs. It serves as an alternative to keyword‑based detectors, using a transformer‑based classifier that returns a binary signal indicating refusal or non‑refusal. Garak Refusal Detector is a fine-tuned version of answerdotai/ModernBERT-base, trained on synthetic refusal and non-refusal data for binary sequence classification.
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