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AI 模型 85003

• Nete-13B-GGUF↗

  TheBloke

• Falcon-H1R-7B-GGUF↗

  tiiuae

  This repository presents **Falcon-H1R-7B**, a reasoning-specialized model introduced in the paper Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling. Built on top of Falcon-H1-7B-Base, it was trained via cold-start supervised fine-tuning with long reasoning traces and further enhanced by scaling RL with GRPO. The model demonstrates outstanding performance across various benchmark evaluations, including mathematics, programming, instruction following, and general logic. For more details about the training protocol of this model, please refer to the Falcon-H1R technical blogpost and Technical Report.

• maxvit_tiny_rw_224.sw_in1k↗

  timm

• res2net50_26w_4s.in1k↗

  timm

• tiny_vit_11m_224.dist_in22k↗

  timm

  A TinyViT image classification model. Pretrained on ImageNet-22k with distillation by paper authors.

• lfm2.5-2.6b-fable5-coding-agent-GGUF↗

  tinyopsec

• jinen-v2-xsmall.gguf↗

  togatogah

• tiny-DbrxForCausalLM↗

  trl-internal-testing

• Deberta_Human_Value_Detector↗

  tum-nlp

  This is a fine-tuned Deberta model to detect human values in arguments. The model is part of the ensemble that was the best-performing system in the SemEval2023 task: Detecting Human Values in arguments It was trained and tested on a dataset of 9324 annotated arguments. The whole ensemble system achieved a F1-Score of 0.56 in the competiton. This model achieves a F1-Score of 0.55. Code for retraining the ensemble is accessible in this repo This model is built on custom code. So the inference api cannot be used directly. To use the model please follow the steps below... To make a prediction and map the the outputs to the correct labels. During the competiton a threshold of 0.25 was used to binarize the output.

• Qwen3.5VL-0.8B-ImageExplainer-GGUF↗

  twinblade02