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AI 模型 449
- maxvit_tiny_tf_224.in1k↗
timm
An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. MaxxViT covers a number of related model architectures that share a common structure including: - CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages. - MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid). - CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate. Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released.
- OpenMed-NER-GenomeDetect-SuperClinical-141M↗
OpenMed
This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. BC2GM corpus targets gene and protein mention recognition from the BioCreative II Gene Mention task. The BC2GM (BioCreative II Gene Mention) corpus is a foundational dataset for gene and protein name recognition in biomedical literature, created for the BioCreative II challenge. This corpus contains thousands of sentences from MEDLINE abstracts with manually annotated gene and protein mentions, serving as a critical benchmark for genomics and molecular biology NER systems. The dataset addresses the challenging task of identifying gene names, which often have complex nomenclature and ambiguous boundaries. It has been instrumental in advancing automated gene recognition systems used in functional genomics research, gene expression analysis, and molecular biology text mining. The corpus continues to be widely used for training and evaluating biomedical NER models.
- Devstral-Small-2507-MLX-bf16↗
lmstudio-community
*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator**: mistralai **Original model**: Devstral-Small-2507 **MLX quantization**: provided by LM Studio team using mlx_lm **LM Studio model page**: mistralai/devstral-small-2507 LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
- Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-GGUF↗
huihui-ai
This is an uncensored version of google/gemma-4-E2B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
- DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-GGUF↗
QuantFactory
language: - en - de - fr - it - pt - hi - es - th - zh - ko - ja license: llama3.1 pipeline_tag: text-generation tags: - roleplay - llama3 - sillytavern - idol - facebook - meta - pytorch - llama - llama-3 extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other This is quantized version of aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored created using llama.cpp - pip install datasets openai - start you openai Server,change Uncensored_Test/harmful_behaviors.py client to you Openai Server address and api_key
- bagel-dpo-34b-v0.2↗
jondurbin
This version also includes the toxic DPO dataset, and should have less censorship than it's counterparts. You may want to use a system prompt like: 1) For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental. 2) After you created your account update your billing and navigate to the deploy page. 3) Select the following - GPU Type: A6000 - GPU Quantity: 2 - Category: Creator - Image: Jon Durbin - Coupon Code: JonDurbin 4) Deploy the VM! 5) Navigate to 'Running Instances' to retrieve instructions to login to the VM 6) Once inside the VM, open the terminal and run `volume=$PWD/data` 7) Run `model=jondurbin/bagel-dpo-34b-v0.2` 8) `sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model` 9) The model will take some time to load... 10) Once loaded the model will be available on port 8080 *Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- MOSS-Image-SFT-Turbo-INT8-ConvRot↗
toxicdog
Experimental model files for ComfyUI and others, mainly created for testing in Radiant Canvas for fast macOS inference (https://apps.apple.com/us/app/radiant-canvas-ai-image-gen/id6802973075). A fast 8-step variant of OpenMOSS-Team/MOSS-Image-SFT. - `diffusion_models/moss_image_sft_turbo_int8_convrot.safetensors`: the diffusion transformer as a single file in ComfyUI's native INT8 ConvRot format.
- wav2vec2-large-robust-12-ft-emotion-msp-dim↗
audeering
Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with audEERING. The model expects a raw audio signal as input, and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on MSP-Podcast (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An ONNX export of the model is available from doi:10.5281/zenodo.6221127. Further details are given in the associated paper and tutorial.
- chinese-wav2vec2-base↗
TencentGameMate
Pretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.