cogito-v1-preview-qwen-14B
deepcogito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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cogito-v1-preview-qwen-32B
deepcogito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 ↗cogito-v2-preview-llama-109B-MoE
deepcogito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 ↗cogito-v2-preview-llama-70B
deepcogito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 ↗PhysBrain1.5-8B
DeepCybo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 ↗humanizer
DeepDive404-2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 ↗MiniMax-H3
Deepdive404-3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 ↗Qwen3.8-27B-OBLITERATED
Deepdive404-3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 ↗This repository contains the conversion of deepdml/whisper-large-v3-turbo to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the `compute_type` option in CTranslate2.
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View details ↗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 ↗gpt2-spanish
DeepESP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 ↗gpt2-spanish-medium
DeepESP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 ↗IF-I-L-v1.0
DeepFloyd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 ↗IF-I-M-v1.0
DeepFloyd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 ↗IF-I-XL-v1.0
DeepFloyd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 ↗IF-II-L-v1.0
DeepFloyd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 ↗IF-II-M-v1.0
DeepFloyd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 ↗t5-v1_1-xxl
DeepFloyd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 ↗mlcd-vit-bigG-patch14-336
DeepGlint-AIMLCD-ViT-bigG is a state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions. We adopted the official LLaVA-NeXT and the official training dataset LLaVA-NeXT-Data for evaluating the foundational visual models.
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View details ↗UniME-Phi3.5-V-4.2B
DeepGlint-AI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 ↗maple-preview
deepgrove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 ↗maple-preview-GGUF
deepgroveTQ1_0 and TQ2_0 are different ternary packing schemes. Use TQ2_0 for generally faster speeds but slightly higher memory. LM-head is kept in higher precision - either Q4_k or FP16. Maple-Preview is a 20B-A1B reasoning model designed from the start for efficient on-device inference. It utilizes a 24-layer, 256-expert (8 active) configuration with 3:1 SWA-512:GA attention. On benchmarks, Maple-Preview sets a new point on the Pareto frontier for both memory-to-performance and speed-to-performance, demonstrating its strong reasoning capabilities. However, we note that this preview is focused primarily on raw reasoning and, as such, may underperform on agentic benchmarks. We intend to continue improving general performance through extended training before Maple's full release.
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View details ↗DeepHat-V1-7B
DeepHatDeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at Deephat.ai or go to Kindo.ai to create agents. DeepHat is a finetune of Qwen2.5-Coder-7B, and inherits the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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View details ↗bald_konverter
deepmancer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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