Eklav-Passage-0.6B
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Eklav-Passage-14B
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View details ↗Eklav-Passage-4B
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View details ↗Eklav-Passage-8B
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View details ↗Eklav-Table-8B
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View details ↗Naive-SFT-Table-8B
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View details ↗Std-SFT-Passage-0.6B
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View details ↗Std-SFT-Passage-4B
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View details ↗Std-SFT-Passage-8B
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View details ↗Std-SFT-Passage-9B
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View details ↗Std-SFT-Table-8B
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View details ↗This is a multimodal, refusal-ablated (commonly described as "uncensored") W4A4 NVFP4 derivative of the reproducible `trohrbaugh/Qwen3.8-27B-heretic-ara` BF16 checkpoint. It is intended for native Blackwell NVFP4 inference. This artifact passed its text-capability, image-vision, benign refusal-surface, native MTP-acceptance, integrity, and clean-load gates on vLLM 0.23. See `BUILD_MANIFEST.json`, `VALIDATION_REPORT.json`, and `SHA256SUMS` for exact provenance and results. Video tensors/processors are preserved, but video input was not part of the live runtime gate. The live gate used `Qwen3_5ForConditionalGeneration`, native three-token MTP, the FlashInfer CUTLASS NVFP4 kernel, an 8,192-token context, and an RTX PRO 6000 Blackwell GPU. Loaded model memory was approximately 19.53 GiB.
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View details ↗addis-scribe-streaming
addisai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-combined
addlabsviral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 ↗WanVideo_comfy
adelcroft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 ↗WanVideo_comfy_fp8_scaled
adelcroft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 ↗Z-Image-GGUF
Ademola265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 ↗fuyu-8b
adeptWe’re releasing Fuyu-8B, a small version of the multimodal model that powers our product. The model is available on HuggingFace. We think Fuyu-8B is exciting because: 1. It has a much simpler architecture and training procedure than other multi-modal models, which makes it easier to understand, scale, and deploy. 2. It’s designed from the ground up for digital agents, so it can support arbitrary image resolutions, answer questions about graphs and diagrams, answer UI-based questions, and do fine-grained localization on screen images. 3. It’s fast - we can get responses for large images in less than 100 milliseconds. 4. Despite being optimized for our use-case, it performs well at standard image understanding benchmarks such as visual question-answering and natural-image-captioning. Please note that **the model we have released is a base model. We expect you to need to finetune the model for specific use cases like verbose captioning or multimodal chat.** In our experience, the model responds well to few-shotting and fine-tuning for a variety of use-cases. Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. We simply treat the transformer decoder like an image transformer (albeit with no pooling and causal attention). See the below diagram for more details.
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View details ↗persimmon-8b-base
adeptAt Adept, we’re working towards an AI agent that can help people do anything they need to do on a computer. We’re not in the business of shipping isolated language models (LMs)—this was an early output of the model scaling program that will support our products. We trained it from scratch using a context size of 16K. Many LM use cases are context-bound; our model has 4 times the context size of LLaMA2 and 8 times that of GPT-3, MPT, etc.
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View details ↗persimmon-8b-chat
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View details ↗Med-Qwen-3.5-4B-Instruct
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