Open-Weight Models

weighted/imatrix quants of https://huggingface.co/gdorane01/XORTRON.CriminalComputing.larger For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/XORTRON.CriminalComputing.larger-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Model · Text generation

Huihui-NeoHorse-1-4B-abliterated

Huihui.ai

This is an uncensored version of TokenRhythm/NeoHorse-1-4B 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. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…

Building and evaluating AI on logistics data. Built with Llama. This checkpoint is a full-parameter fine-tune of meta-llama/Llama-3.2-1B-Instruct, published with the data split, evaluation results, and behavioral tests used to inspect it. The project connects a working training-and-inference pipeline with a retrospective audit of what its score demonstrates. The checkpoint and a depth-2 decision tree both score 100% on the same historical 200-row split. A rule using two supplied fields reproduces every label in the 1,000-row source table. Prompt rewrites reveal additional response failures, including sensitivity to irrelevant text. These findings make the checkpoint useful for studying…

Model

compression_autoencoder

George Perdrizet

A convolutional autoencoder trained to compress 256×256 RGB images into a compact 1024-dimensional latent representation, achieving 192× compression ratio. This model learns to compress high-quality images by encoding them into a compact latent space, then reconstructing them with minimal quality loss. The encoder reduces a 196,608-value image (256×256×3) to just 1024 numbers, while the decoder reconstructs the original image from this compressed representation. This model is designed for educational purposes to demonstrate how autoencoders can learn compression automatically from data, rather than using hand-crafted rules like JPEG or PNG. Trained on DF2KOST, a combined dataset of 26.8k…

Model · Tabular classification

heart_failure_prediction

Abdalla Ahmed

A Keras / TensorFlow neural network that predicts the presence of heart disease in patients using 11 clinical and demographic features. Educational and research purposes only. Explore how clinical features relate to heart disease risk. - Not a medical device. Do not use for real clinical decisions. - Not validated on real-world hospital populations. from huggingfacehub import hfhubdownload import joblib import pandas as pd from tensorflow import keras modelpath = hfhubdownload("abdalla732/heartfailureprediction", "heartmodel.keras") scalerpath = hfhubdownload("abdalla732/heartfailureprediction", "scaler.joblib") colspath = hfhubdownload("abdalla732/heartfailureprediction"…

Model · Tabular classification

DigitalEduTransformers

SnowFlash383935

Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох. Пример вывода: [False, True]

Whisper finetune for Japanese focused on general/anime domains. For usage instructions follow openai/whisper-large-v3-turbo. Due to vocab changes ctranslate2>=4.7.1 required for faster-whisper. For inference engines with hardcoded vocab, the token embedding can be padded. Finetuned from base with pruned vocab and encoder conv adaption, indices can be found in mapping.txt. Trained decoder only for 2^20 steps, batch size 64. Using a 45000 hour corpus (largest source is 17000 of filtered reazonspeech-all) with custom mixing ratio and augmentation to maintain long form performance and timestamps. Benchmarks. Competitive for size on test sets, particually good on JSUT-book. Also trained for…

A hybrid Graph Neural Network (GraphSAGE + GATv2) trained on labeled Solana blockchain datasets (SolRPDS, Kaggle Solana, Solarchive) to detect malicious liquidity pools, rug-pull tokens, and fraudulent wallet flows. 1. SolRPDS (Solana Rug Pull Dataset): 62,895 suspicious liquidity pools and 22,195 confirmed rug-pull tokens derived from 3.69 billion transactions. 2. Kaggle Solana Blockchain Dataset: Labeled entity categorization. 3. Solarchive: Partitioned daily Solana Parquet transaction flows.

Model

jojo-sovereign-trained

Mohammed Abdullah H Abdulaziz

This model was finetuned and converted to GGUF format using Unsloth. - Llama-3.2-3B-Instruct.Q4KM.gguf An Ollama Modelfile is included for easy deployment. The model's BOS token behavior was adjusted for GGUF compatibility. This was trained 2x faster with Unsloth

Model · Image and text to text

Potato-CODER-24GB

Jan Slominski

A 27B coding model that runs on one 24 GB NVIDIA card at the full 262,144-token context, with multi-token-prediction speculation and vision still on. It is Swift-Qwen3.8-27B (Qwen3.8-27B post-trained by UkisAI for shorter reasoning), requantized with a coding-calibrated importance matrix. Nothing was trained here: this is a quantization and a deployment recipe, measured end to end. IQ4XS/Potato-CODER-24GB-IQ4XS.gguf (the model, MTP head embedded), mmproj-Potato-CODER-24GB-Q80.gguf (vision projector, Q80 of UkisAI's F16 projector; the vision tower is unchanged from Qwen3.8-27B), LICENSE (Swift Open License), LICENSE-APACHE-2.0 (base model), NOTICE, SHA256SUMS. Use our llama.cpp branch…

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.19.1

Model · Tabular classification

collaborative-filtering-movielens

Keras

This repo contains the model and the notebook on how to build and train a Keras model for Collaborative Filtering for Movie Recommendations. Full credits to Siddhartha Banerjee. Based on a user and movies they have rated highly in the past, this model outputs the predicted rating a user would give to a movie they haven't seen yet (between 0-1). This information can be used to find out the top recommended movies for this user. The dataset consists of user's ratings on specific movies. It also consists of the movie's specific genres. The model was trained for 5 epochs with a batch size of 64. The following hyperparameters were used during training: - trainingprecision: float32

Model · Tabular classification

human-disease-prediction

AWeirdDev

Try it on Spaces The yourdata variable should be a vector of zeros and ones. A zero means "False," and a one means "True." Create a vector that pairs with the following symptoms, then the model will predict what disease it might be. It has been reported as 1.0 (100%), but I don't believe it.

Model · Tabular classification

fela-tab

Lowdown Labs

This model is a research preview. It is offered for advancing public science and for evaluation. It is not a substitute for a domain expert, and it is not a certified decision system. FelaTab reads a small table you already have and predicts the missing cells. You give it some example rows with their answers (the support rows) and one or more rows you want filled in (the query rows); it learns the pattern from your examples in a single pass and returns the answer with a calibrated confidence range. There is no per table training, no fitting, and no setup: you point it at a table and it predicts. It runs on a plain CPU with no GPU. It is a prior fitted network (a "foundation model for…

This is a LinkTransformer model. At its core this model this is a sentence transformer model sentence-transformers model- it just wraps around the class. It is designed for quick and easy record linkage (entity-matching) through the LinkTransformer package. The tasks include clustering, deduplication, linking, aggregation and more. Notwithstanding that, it can be used for any sentence similarity task within the sentence-transformers framework as well. It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. Take a look at the documentation of sentence-transformers if you want to use this model for more than what we…

Model · Speech recognition

koelsch-wav2vec2-ipa

Chem

IPA phoneme recognition for Kölsch (Ripuarian German, Cologne). Fine-tuned from facebook/wav2vec2-xls-r-300m with a CTC head over a 48-symbol IPA inventory. This is also the forced-alignment model. Its frame-level CTC posteriors drive torchaudio.functional.forcedalign to produce Praat TextGrids with word and phone tiers — see notebook 9 in the repository. Trained during the CIF Tandem Fellowship at IfL-Phonetik, University of Cologne, on Alles Kölsch (Bhatt & Lindlar 1998) — 4,670 utterances, 4.5 hours of spontaneous Cologne dialect speech from 105 speakers aged 10–88 across 49 neighbourhoods. Kölsch is Ripuarian German. It has no public speech dataset and no standardised spelling: 94.1 %…

Whisper finetune for Japanese focused on general/anime domains. For usage instructions follow openai/whisper-large-v3-turbo. Due to vocab changes ctranslate2>=4.7.1 required for faster-whisper. For inference engines with hardcoded vocab, the token embedding can be padded. Finetuned from tiny with pruned vocab and encoder conv adaption, indices can be found in mapping.txt. Trained decoder only for 2^19 steps, batch size 64. Using a 45000 hour corpus (largest source is 17000 of filtered reazonspeech-all) with custom mixing ratio and augmentation to maintain long form performance and timestamps. Benchmarks. CER roughly between OpenAI whisper-base/small, not great but also the smallest model…

Model

opus-4.8-recreation-1b-light

CyberviserAI

Latest local refresh: see opus-4.8-recreation-1b-light-v4 under the same org (glasseye RTX 5070).

This model is a diffusion model for unconditional image generation of cute.

Model · Image segmentation

toxoplasma-plaque-well-detector-yolo11

Einar Olafsson

Locates wells in whole-plate and multi-well crystal violet plaque-assay images. The front half of a two-stage pipeline with Toxoplasma Plaque v1; the well it finds also gives the diameter that makes areas comparable across microscopes. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Toxoplasma Plaque Well Detector v1 in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. This is a plaque-assay model and is driven by…

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.18.1