open-image-preferences-v1-flux-dev-lora
data-is-better-together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。
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View details ↗DISCOVER & COMPARE
Explore AI models 84991
DeepSeek-R1-Distill-Llama-8B-Abliterated-GGUF
data-pioneer-0826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。
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View details ↗BSL-160M
Data-Selection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。
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View details ↗BSL-470M
Data-Selection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。
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View details ↗PDS-1.7B
Data-Selection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。
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View details ↗FLUX-MonochromeManga
dataautogpt3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。
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View details ↗OpenDalleV1.1
dataautogpt3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。
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View details ↗ProteusV0.2
dataautogpt3merged with RealCartoonXL to fix issues with inability to understand tags related to anime or cartoon styles at just a weight of 0.5% out of 100% using custom scripts with slerp like methods. Version 0.2 shows subtle yet significant improvements over Version 0.1. It demonstrates enhanced prompt understanding that surpasses MJ6, while also approaching its stylistic capabilities. Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.
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View details ↗ProteusV0.3
dataautogpt3Proteus V0.3 has been advanced with an additional 200,000 anime-related images, further refined by a selection of 15,000 aesthetically pleasing images, enhancing its lighting effects significantly. This upgrade preserves its understanding of prompts and maintains its photorealistic and stylistic capabilities without suffering from catastrophic forgetting. Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs. In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations.
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View details ↗ProteusV0.4
dataautogpt3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。
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View details ↗DBRX Instruct
Databricks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。
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View details ↗piper-en_AU-librivox-medium
DataCraftsmanAustralia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。
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View details ↗tolkio-models
datacrunchThe models the Tolkio app (an offline speech translator for Android) downloads. Each keeps its own license: Install from a folder instead of downloading: copy this folder to the phone and use "Import from a folder" in the app.
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View details ↗Toto-2.0-1B
DatadogToto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
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View details ↗Toto-2.0-2.5B
Datadog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。
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View details ↗Toto-2.0-22m
DatadogToto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
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View details ↗Toto-2.0-313m
DatadogToto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
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View details ↗Toto-2.0-4m
DatadogToto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
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View details ↗Toto-Open-Base-1.0
DatadogToto (Time Series Optimized Transformer for Observability) is a **state-of-the-art** time-series foundation model designed for multi-variate time series forecasting, emphasizing observability metrics. Toto efficiently handles high-dimensional, sparse, and non-stationary data commonly encountered in observability scenarios. The average rank of Toto compared to the runner-up models on both the GIFT-Eval and BOOM benchmarks (as of May 19, 2025). - **Zero-Shot Forecasting**: Perform forecasting without fine-tuning on your specific time series. - **High-Dimension Multi-Variate Support**: Efficiently process multiple variables using Proportional Factorized Space-Time Attention. - **Decoder-Only Transformer Architecture**: Support for variable prediction horizons and context lengths. - **Probabilistic Predictions**: Generate both point forecasts and uncertainty estimates using a Student-T mixture model. - **Extensive Pretraining on Large-Scale Data**: Trained on over 2 trillion time series data points, the largest pretraining dataset for any open-weights time series foundation model to date. - **Tailored for Observability Metrics with State-of-the-Art Performance** on GIFT-Eval and BOOM.
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View details ↗freshwater-quantization-models
Datadragon110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。
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View details ↗freshwater-salmon-analysis-step3
Datadragon110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。
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View details ↗freshwater-salmon-analysis-step5
Datadragon110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。
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View details ↗camembert-base
DataikuNLP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。
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View details ↗chandra
datalab-toChandra is an OCR model that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. You can try Chandra in the free playground here, or at a hosted API here. - Convert documents to markdown, html, or json with detailed layout information - Good handwriting support - Reconstructs forms accurately, including checkboxes - Good support for tables, math, and complex layouts - Extracts images and diagrams, with captions and structured data - Support for 40+ languages
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