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AI Models 449
- malicious-url-detection↗
kmack
Using this model, you can detects harmful links created to harm people such as phishing, malware urls. this model Classifies if the urls addresses are malware and benign. Type the domain name of the url address in the text field for classification in API: Like this: "huggingface.com"
- coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k↗
timm
A timm specific CoAtNet (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k 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.
- animagine-xl-4.0-GGUF↗
off-grid-ai
GGUF conversions of Animagine XL 4.0 for **on-device** image generation with stable-diffusion.cpp. Converted with sd.cpp's `-M convert` so the tensors are correctly named and load directly — the community GGUF quants of this model are mis-exported and fail `get sd version from file`. `Q4_K_S` in a file name: stable-diffusion.cpp wrote the file with `--type q4_K`. Its weight tensors are Q4_K and the other tensors stay F16/BF16/F32. `Q4_K_S` is the closest standard GGUF label, so Hugging Face can list the file. This file was named `animagine-xl-4.0-Q4_K.gguf` before 2026-10-21; the contents are unchanged, and links pinned to an earlier commit still work. **Original model:** https://huggingface.co/cagliostrolab/animagine-xl-4.0 — created by its respective authors. **License:** `openrail++` (carried over from the original; its use restrictions apply). This is a format conversion (quantization) only; all credit for the model belongs to the original creators.
- Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MTP-GGUF↗
huihui-ai
This is an uncensored version of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled 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. Please use the latest version of ggml-org/llama.cpp - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
- CarbonVillain-en-10.7B-v4↗
jeonsworld
This model is an experimental version created using mergekit. - merge models - jeonsworld/CarbonVillain-en-10.7B-v1 - jeonsworld/CarbonVillain-en-10.7B-v2 - method: slerp
- Huihui-Qwen3.8-27B-abliterated-GGUF↗
NilVoid
This is an uncensored version of Qwen/Qwen3.8-27B 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. The newly added **Huihui-Qwen3.8-27B-abliterated-Swift** series come from ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. This is just a test/validation. The newly added **Huihui-Qwen3.8-27B-abliterated-Ternary** series come from prism-ml/Ternary-Bonsai-2-27B-gguf. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. The size after conversion may differ from the original GGUF (Some of the weights are converted from PTQ1 to Q2_K or Q3_K.). This is just a test/validation.
- Swift-1.5-4bit-MLX↗
ukisai
Website • Learn more • BF16 model • GGUF • GSQ-RCO GGUF • Evaluation • Enterprise licensing **Apple MLX 4-bit affine quantization.** This is Swift 1.5 in native MLX format, converted with the official Apple MLX-LM converter using 4 bits and group size 64. Swift 1.5 is UkisAI's reasoning-efficient Qwen3.8-27B derivative, focused on stronger long-horizon, agentic and coding performance while using fewer thinking tokens. **Starting from Homebrew or seeing `Received 501 parameters not in model`?** Use QUICKSTART.md to install the required runtime and create an explicit `serve` launcher. It reuses your existing model directory, including an HF cache snapshot. A bare `mlx_lm.server` command may select a separate Homebrew installation that lacks the Swift architecture and cache patches.
- alias-gpt2-small-x21↗
stanford-crfm
- **Model type:** Text Generation - **Language(s) (NLP):** More information needed - **License:** Apache 2.0 - **Parent Model:** GPT-2 - **Resources for more information:** - GitHub Repo The model should not be used to intentionally create hostile or alienating environments for people. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
- Nous-Hermes-2-Yi-34B↗
NousResearch
Nous Hermes 2 Yi 34B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. 1. Example Outputs - Discussing the Laws of Gravity - Create a Flask based FTP Server 2. Benchmark Results - GPT4All - AGIEval - BigBench - Averages Compared 3. Prompt Format 4. Quantized Models Nous-Hermes 2 on Yi 34B outperforms all Nous-Hermes & Open-Hermes models of the past, achieving new heights in all benchmarks for a Nous Research LLM as well as surpassing many popular finetunes.
- Cydonia-24B-v4.3-GGUF↗
TheDrummer
Hi! I'm Drummer. I'm a Software Engineer with experience in JavaScript, Golang, Python, and generally engineering the crap out of things. - **Exploration:** Everyone is trying to figure out how AI works and what it's capable of. I am too - just not in creating the smartest, safest model at all costs. - **Upskill:** The world is headed towards AI. It is here to stay. This has been my way of brushing up in this new form of computing challenge. - **Value:** I yearn to create value. I feel satisfaction and fulfillment in providing something meaningful for others. - **Fun:** It's just fun using and making models. It's also fun coming up with theories and realizing them in practice (training AI). I started my tuning venture back in mid-2024 when I wanted to improve its literary capabilities. I've come a long way since then and I have branched out and specialized. Foundational models today are optimized for non-creative uses, and I believe there is a place for AI in creativity and entertainment.