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84,991 model profiles13202 providers / publishers45 capability categories465 with comparable prices · 30 benchmarked
TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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This is a coder/programming model, will full reasoning on the Qwen 3 platform that is insanely fast - hitting over 150 t/s on moderate hardware, and 50 t/s+ on CPU only... This is a generalist coding model, good for code blocks, brainstorming coding ideas, and generating draft code fast. It contains 42 layers (a merge of TWO 0.6B models), and 464 tensors - a very dense model for this size.

Text generation
Input Text→ Output Text
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This is a coder/programming model, will full reasoning on the Qwen 3 platform that is insanely fast - hitting over 150 t/s on moderate hardware, and 50 t/s+ on CPU only... This is a generalist coding model, good for code blocks, brainstorming coding ideas, and generating draft code fast. Version 2 is more stable the V1, and generates better code. Also see revised, and narrowed settings for best results too.

Text generation
Input Text→ Output Text
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Creative, Brainstorming, Roleplay, Long Form, Story, Scene, And Fiction Generation: Model Version 1, Formula 1 This model is a mixture of 4 highly creative models using a very different form of merging: composite. This model EXCELS in long form [often exceeding 1500 tokens - without being told to do so], highly creative output with a minimum of input. (see samples below)

Text generation
Input Text→ Output Text
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FooocusPlus

DavidDragonsage

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Category pending
Input Not verified→ Output Not verified
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Rhea-72b-v0.5

davidkim205

The Rhea project is a project that conducts research on various learning methods to improve llm model performance. We fine-tuned the existing model using the nox framework. We built a dataset for SFT learning based on the currently open dataset, and created a dataset using SGD (Self-Generated Dataset Creation Method for DPO Learning) for DPO learning. This method proposes a novel method for generating datasets for DPO (Self-supervised Learning) models. We suggest a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO models. Randomly selecting data from each category within the training dataset, we constructed a DPO (Direct Preference Optimization) dataset using sentences with logits lower than the mean within the model-generated sentences. * I'm sorry I can't reveal it.

Text generation
Input Text→ Output Text
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Visual QA
Input Text · Image→ Output Text
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GGUF conversions of **NetEase Youdao Confucius4-R2T2**, a low-latency, append-only streaming ASR model (a Qwen3-ASR-1.7B fine-tune with Longest Stable Prefix decoding), ready to run with audio.cpp — no Python, no PyTorch, local inference on Metal / CUDA / CPU. * Original model: netease-youdao/Confucius4-R2T2 · source code · demo * Runtime: audio.cpp (`ggml`), family `confucius4_r2t2` These files use the **`confucius4_r2t2`** family and embed its schema-v1 model specification. Use a build containing the family rename and streaming-delta fix from audio.cpp PR #604 (validated at commit `40dec7147128649a4180a5c982a7808cd272fc3e`). Older builds that only register `r2t2_asr` need to be updated. Model-manager package IDs and session-option prefixes use `confucius4_r2t2` as shown below; the GGUF filenames remain unchanged.

Speech recognition
Input Audio→ Output Text
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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