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AI 模型 84757
- ZwZ-8B-i1-GGUF↗
mradermacher
- MiniMax-M2.7-DFlash↗
NVIDIA
- tmr-ai-text-detector-ONNX↗
onnx-community
This is an ONNX version of Oxidane/tmr-ai-text-detector. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for `text-classification`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline A robust AI-generated text detector based on RoBERTa-base, trained with Focal Loss and Self-Hard-Negative iterative mining on the RAID dataset.
- Grug-67B-A2B-Dr-Doom-Science-SFT-Step5989-2026.10.02↗
open-athena
- OpenMed-ZeroShot-NER-Species-Base-220M↗
OpenMed
**Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.
- tiny-random-DonutSwinModel↗
optimum-intel-internal-testing
- tiny-random-BertForSequenceClassification↗
peft-internal-testing
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- FrogNano-4B-2609-GGUF↗
prithivMLmods
- distilgpt2-finetuned-python_code_instructions_18k_alpaca-GGUF↗
QuantFactory
- Qwen3-235B-A22B-Instruct-2507-FP8↗
阿里巴巴 · Qwen
We introduce the updated version of the **Qwen3-235B-A22B-FP8 non-thinking mode**, named **Qwen3-235B-A22B-Instruct-2507-FP8**, featuring the following key enhancements: - **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**. - **Substantial gains** in long-tail knowledge coverage across **multiple languages**. - **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation. - **Enhanced capabilities** in **256K long-context understanding**. This repo contains the FP8 version of **Qwen3-235B-A22B-Instruct-2507**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 235B in total and 22B activated - Number of Paramaters (Non-Embedding): 234B - Number of Layers: 94 - Number of Attention Heads (GQA): 64 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.