Feature Extraction Models

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

Model · Feature extraction

Ovis-Omni-Embedding-3B

ATH-MaaS

Ovis-Omni-Embedding-3B is a 3B-parameter universal embedding model for text, images, visual documents, video, audio, and interleaved multimodal inputs. It maps every supported input type into one coherent representation space, enabling any-to-any retrieval with a single encoder. The model is initialized from Qwen2.5-Omni-3B. Rather than attaching separate modality-specific embedding towers, it retains the native text tokenizer, vision encoder, audio encoder, and shared Thinker backbone. The speech-generation Talker and language-modeling head are removed, and the final-layer hidden state at the last non-padding token is used directly as the retrieval embedding. Ovis-Omni-Embedding-3B is…

Model · Feature extraction

Ovis-VL-Embedding-2B

ATH-MaaS

Ovis-VL-Embedding-2B is a compact vision-language embedding model for text, images, visual documents, video, and interleaved multimodal inputs. It maps every supported input type into one coherent representation space, enabling cross-modal retrieval with a single 2B-scale encoder. The model is initialized from Qwen3.5-2B. It retains the native text and vision encoders together with the shared multimodal language backbone, removes the language-modeling head, and directly uses the final-layer hidden state at the last non-padding token as the retrieval embedding. No modality-specific projection head is added. Ovis-VL-Embedding-2B is designed for efficient multimodal search, multimodal RAG…

Model · Feature extraction

Ovis-VL-Embedding-9B

ATH-MaaS

Ovis-VL-Embedding-9B is a high-capacity vision-language embedding model for text, images, visual documents, video, and interleaved multimodal inputs. It maps every supported input type into one coherent representation space, enabling high-accuracy cross-modal retrieval with a single encoder. The model is initialized from Qwen3.5-9B. It retains the native text and vision encoders together with the shared multimodal language backbone, removes the language-modeling head, and directly uses the final-layer hidden state at the last non-padding token as the retrieval embedding. No modality-specific projection head is added. Ovis-VL-Embedding-9B is designed for high-quality multimodal search…

Model · Feature extraction

patent_retriever_multilingual_nemotron_v0.1

Elplaguister

Dense retriever for patent prior-art search (abstract → abstract), fine-tuned from on patent-family pairs (same invention filed in different jurisdictions / languages). Trained for Korean ↔ English cross-lingual and monolingual patent retrieval. Do not use the base model's asymmetric convention (query: / passage: ). It raises no error but silently degrades retrieval quality, because training was symmetric. corpus (other family members masked; one relevant document per query). crosssame: translation pairs (KR↔EN), crosssimilar: cross-lingual same-family non-translation pairs, monosimilar: monolingual same-family pairs. Scores on the 50k-document subset; full-corpus (209,671 documents)…

PhaGen-0404-step12800 is the frozen paper model: 152,421,601 parameters, three hierarchical stages with dimensions 512/256/196, maximum token canvas 131,072, and a nine-token character-level DNA tokenizer. This repository contains the standalone inference weights, tokenizer, model configuration and custom code. Weights are the selected April 2026 run's global step 12,800, not the final saved checkpoint or a later training implementation. Use PyTorch 2.5, Transformers 4.54.1, einops 0.8, beartype 0.22, accelerate and safetensors. The PhaGen source repository also provides an installable phagen CLI and an agent skill. Pin a Hub commit hash with revision for repeatable loading. Custom model…

Model · Feature extraction

pplx-embed-v2-late-0.6b-ONNX

Nicolas Embleton

Exported to ONNX from perplexity-ai/pplx-embed-v2-late-0.6b (by Perplexity, MIT license). This is an unofficial conversion; all credit for the model goes to the original authors. The model is a late-interaction (multi-vector) retriever: it outputs one L2-normalized 128-d vector per token or merged image patch, and documents are scored against queries with MaxSim. - embedtokens: inputids -> inputsembeds - visionencoder: pixelvalues, gridthw (one image per call) -> image embeddings that replace the image tokens - textmodel: inputsembeds, attentionmask, positionids [3,B,T] -> tokenembeddings [B,T,128] Browser runtime: the fp32, fp16, q4 files loaded and ran with onnxruntime-web 1.30.0 (wasm…

本仓库提供由 Qwen/Qwen3-Embedding-4B 转换得到的 RKNN EMBEDDING 模型。 - Model ID：RKNNAI/RK182X-EMBEDDING-Qwen3-Embedding-4B - 模型显示名称：RK182X-EMBEDDING-Qwen3-Embedding-4B - 源模型：Qwen/Qwen3-Embedding-4B - 模型类型：EMBEDDING ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。

The Sovereign-Swarm-Coherence-v1-alpha repository contains the lightweight ONNX & WebAssembly (WASM) client-side package for running inference directly inside web browsers and mobile apps with 0ms server latency and zero cloud API infrastructure costs. - 100% Offline Capable: Zero network dependencies after initial WASM module download. Apache 2.0 License © ItsNotAI LABS

Model · Feature extraction

TeraAnaly

Tera

Russian speech analysis with one frozen w2v-BERT encoder and independently callable age, gender, emotion, stress and CTC components. Decode audio once, run the encoder once, and reuse its output for any heads you need. Noncommercial use only for the TeraAnaly code and trained heads, under CC-BY-NC-SA-4.0. The original encoder weights retain their upstream CC-BY-4.0 license. See LICENSE.md. Outputs are an integer age in years, the highest gender label, the highest emotion label, and a transcript with + before stressed vowels. Emotion labels are neutral, angry, positive, sad, other; demographic labels are female, male, child (child is an age category inherited from the teacher).…

Model · Feature extraction

Thaki-SecEmbed-8B-Preview

ThakiCloud

Security-domain specialized experimental preview. Headline. Security-domain specialized experimental preview. No statistically clear difference from the stock Qwen3-Embedding-8B baseline under the preregistered held-out evaluation. On general retrieval: no catastrophic regression under the preregistered 10% threshold; significant FiQA degradation was observed. Thaki-SecEmbed-8B is a LoRA specialization of Qwen/Qwen3-Embedding-8B. It retrieves ATT&CK technique documentation for security-analyst queries. Queries can be questions, claims, keywords, evidence requests, analyst searches, or TTP hypotheses, in English or Korean. - ATT&CK technique retrieval. Given a query, rank ATT&CK technique…

This is the model card of a transformers model that has been pushed on the Hub. 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).

This is the model card of a transformers model that has been pushed on the Hub. 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).

This is the model card of a transformers model that has been pushed on the Hub. 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).

This is the model card of a transformers model that has been pushed on the Hub. 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).

Model · Feature extraction

ToarimEmbedder

Manuel Antonio Camacho Reyes

TóarimEmbedder es una arquitectura algorítmica de embeddings con estado (stateful) diseñada específicamente para resolver la amnesia contextual y la pérdida de continuidad en sistemas de Recuperación Aumentada por Generación (RAG) aplicados a corpus técnicos extensos y altamente regulados (manuales de maquinaria pesada, compendios normativos, bitácoras operativas y expedientes legales). A diferencia de los modelos basados en Transformers convencionales con atención densa cuadrática $O(n^2)$, TóarimEmbedder opera con complejidad lineal $O(n)$ en procesadores convencionales (CPU), permitiendo a las organizaciones indexar internamente su propiedad intelectual de forma ágil, blindando sus…

Model · Feature extraction

UltraBERT-Embed-150M-ONNX

Pastel Labs

ONNX export of dmis-lab/UltraBERT-Embed-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared the sentence embedding (first-token pooling, as in the sentence-transformers config) with cosine similarity. Fifteen texts are…

Model · Feature extraction

voxtral-mini-audio-extractor

Vxltxr Llc

A lightweight standalone audio feature extractor derived from Voxtral-Mini-3B-2507. This repository isolates the Whisper-based audio encoder and multi-modal projector from the original 3B language model. The extracted module is intended for offline audio preprocessing, dataset preparation, feature caching, and downstream multimodal training pipelines. The language-model decoder is not included. It does not contain the 3B LLaMA language-model decoder. For offline preprocessing, loading the complete multimodal language model is unnecessary when the only required output is the projected audio representation. This standalone checkpoint can therefore be used as a dedicated audio feature…

This model is a fine-tuned version of JetLM/SDAR-8B-Chat-b8 on the sdarwebshopbs4eighthreason dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 4 - totaltrainbatchsize: 4 - totalevalbatchsize: 32 - lrschedulertype: constantwithwarmup - lrschedulerwarmupratio: 0.03 - numepochs: 1.0 - Transformers 4.52.4 - Pytorch 2.9.1+cu129 - Datasets 3.6.0 - Tokenizers 0.21.1

Unofficial GGUF conversion of tencent/WeMM-Embedding-2B for llama.cpp. It is the default embedding model of imgsearch, a local-first image search app. Not affiliated with or endorsed by Tencent. All credit for the model goes to the WeChat Vision Team; it is licensed under Apache 2.0. Both files are needed for image input. Output: 2048 dimensions, L2-normalize; Matryoshka truncation (re-normalize) to 1024/512/256 works well. The prompt must match the reference processor output exactly. No system turn, no instruction; queries and documents are encoded the same way. Take the last-token hidden state (pooling none, last position, or --pooling last): is the llama.cpp mtmd media marker; mtmd adds…

Model · Feature extraction

whisper-cluster

Keisuke Miyamoto

This repository contains a 500-cluster K-means codebook for Whisper large-v3-turbo encoder representations, trained on Japanese audio from Lambda Audio Corpus. Each 1,280-dimensional audio frame can be assigned to its nearest cluster center to produce a discrete unit ID from 0 to 499. These IDs can be used as targets for speech representation learning. They are learned acoustic units, not text tokens or labeled phonemes. The codebook was trained on 18,000,004 sampled frames from 199.89 hours of accepted audio. The repository includes the cluster centers, training features, and metadata. Original audio and transcripts are not included. The files are stored under codebook/. Only centroids.npy…