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AI 모델 84759

• 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

• uk-punctcase↗

  ukr-models

• Qwen3.8-27B-AWQ-INT4↗

  ulkaa

• Aura-Medium-v1.1-GGUF↗

  UncannyEcho

• gpt-oss-120b-unsloth-bnb-4bit↗

  unsloth

  Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of the open models: - `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters) - `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.

• granite-4.1-30b-GGUF↗

  unsloth

• Qwen3-VL-32B-Instruct-unsloth-bnb-4bit↗

  unsloth

• Qwen3-VL-8B-Thinking-1M-GGUF↗

  unsloth