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AI 模型 450
- financial-roberta-large-sentiment↗
soleimanian
Financial-RoBERTa Financial-RoBERTa is a pre-trained NLP model to analyze sentiment of financial text including: Financial Statements, Earnings Announcements, Earnings Call Transcripts, Corporate Social Responsibility (CSR) Reports, Environmental, Social, and Governance (ESG) News, Financial News, Etc. Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text. The model will give softmax outputs for three labels: Positive , Negative or Neutral . How to perform sentiment analysis: The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:
- Huihui-Qwen3.8-27B-abliterated-GGUF↗
ahmed-amedo
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-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and 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. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
- ner-bert-base-cased-pt-lenerbr↗
pierreguillou
**ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-base-cased-pt-lenerbr on the dataset LeNER_br by using a NER objective. Due to the small size of BERTimbau base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*): - **f1**: 0.8926146010186757 - **precision**: 0.8810222036028488 - **recall**: 0.9045161290322581 - **accuracy**: 0.9759397808828684 - **loss**: 0.18803243339061737 **Note**: the model pierreguillou/bert-base-cased-pt-lenerbr is a language model that was created through the finetuning of the model BERTimbau base on the dataset LeNER-Br language modeling by using a MASK objective. This first specialization of the language model before finetuning on the NER task improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model BERTimbau base (a non-specialized language model): - **f1**: 0.8716487228203504 - **precision**: 0.8559286898839138 - **recall**: 0.8879569892473118 - **accuracy**: 0.9755893153732458 - **loss**: 0.1133928969502449
- MiniMax-H3-X2-Detail-VAE↗
speach1sdef178
> **Status:** Working model + tested custom node + workflow + research report\ > **Starting point:** my own already-working MiniMax H3 2X VAE\ > **Scope:** this report intentionally begins with that completed 2X VAE. I created the base 2X VAE used for this project as well, but how that VAE was built is a separate story and is not covered here. This repository is the final practical outcome of the experiment. It contains a **2-in-1 MiniMax H3 X2 VAE**, an optional ComfyUI detail-enhancement node, an example workflow, and the full research article below. Load `MiniMax-H3-X2-Detail-v1.safetensors` with the normal ComfyUI **Load VAE** node and use it as the video VAE for MiniMax H3. For the actual video decode, replace the base ComfyUI VAE Decode node with **`MiniMax H3 VAE Decode (fast)`** from TripleHeadedMonkey/ComfyUI-MiniMaxH3_LatentUpscaler. This node is required for the released 2X workflow.
- deepseek-coder-33B-instruct-GPTQ↗
TheBloke
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches.
- lilt-xlm-roberta-base↗
nielsr
This model is created by combining the Language-Independent Layout Transformer (LiLT) with XLM-RoBERTa, a multilingual RoBERTa model (trained on 100 languages).
- 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.