Holo4-27B
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Holo4-27B-FP8
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View details ↗Holo4-27B-GGUF
Hcompany> [!IMPORTANT] > *Holo4* family: > > - Holo4-27B: Qwen3.8 dense architecture. BF16, FP8, NVFP4, and Q4 GGUF [current]. > - Holo4-35B-A3B: Qwen3.6 MoE architecture. BF16, FP8, NVFP4, and Q4 GGUF. > - Holotron4-30B-A3B: NemotronH Nano Omni architecture. BF16 and FP8. *Holo4*-27B-GGUF is a vision-language model (VLM) for Computer Use, built on Qwen3.8-27B and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool results to the model, then execute its requested clicks, typing, code, and tool calls. Parameters 27B dense Maximum context length in config 262,144 tokens Model type Visual Language Model (VLM) Architecture Qwen3.8 dense Interfaces Graphical interface, code, and tool calls Target environments Web, desktop, and mobile
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View details ↗Holo4-27B-NVFP4
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View details ↗Holo4-35B-A3B
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View details ↗Holo4-35B-A3B-FP8
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View details ↗Holo4-35B-A3B-GGUF
Hcompany> [!IMPORTANT] > *Holo4* family: > > - Holo4-27B: Qwen3.8 dense architecture. BF16, FP8, NVFP4, and Q4 GGUF. > - Holo4-35B-A3B: Qwen3.6 MoE architecture. BF16, FP8, NVFP4, and Q4 GGUF [current]. > - Holotron4-30B-A3B: NemotronH Nano Omni architecture. BF16 and FP8. *Holo4*-35B-A3B-GGUF is a vision-language model (VLM) for Computer Use, built on Qwen3.6-35B-A3B and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool results to the model, then execute its requested clicks, typing, code, and tool calls. Model ID Hcompany/Holo4-35B-A3B-GGUF Architecture Qwen3.6 MoE Checkpoint format Q4_K_M GGUF Maximum context length in config 262,144 tokens
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View details ↗Holo4-35B-A3B-NVFP4
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View details ↗Holotron4-30B-A3B
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View details ↗Holotron4-30B-A3B-FP8
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View details ↗NeoMME-260M
HcompanyNeoMME is an efficient Multilingual and Multimodal-native foundational Encoder. Text tokens and raw image patches pass through one shared Transformer encoder. NeoMME does not use a separately pretrained vision encoder or a causal language model. NeoMME-260M is a pretrained encoder backbone and cannot be used on its own for a downstream task. It returns contextual token representations, so users should fine-tune a task-specific head for retrieval, classification, extraction, or another downstream task. For document retrieval, use NeoMME-260M-Retriever. Parameters 263M Vocabulary 131,072 tokens Context length 16,384 tokens Hidden size 1,024 Image patches 32 × 32 pixels, up to 2,048 pixels on the longest side (default)
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View details ↗NeoMME-260M-Retriever
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View details ↗NeoMME-260M-Retriever-ST-dense
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View details ↗NeoMME-260M-Retriever-ST-late
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View details ↗NeoMME-800M-Retriever
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View details ↗NeoMME-800M-Retriever-ST-dense
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View details ↗NeoMME-800M-Retriever-ST-late
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View details ↗qwen-image-edit-plus-nsfw-lora
Hcris214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 ↗hdd3
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View details ↗Qwen3-VL-8B-NSFW-Caption-V4-gguf
HDiffusion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 ↗tradfusion-v2-training-files
hdparmar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 ↗gpt-2-pubmed-medium
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View details ↗minimax-h3-loras
Hearmeman**Free ComfyUI workflows, Runpod templates, and guides:** 👉 hearmemanai.com A mirror of the MiniMax-H3 LoRAs published on CivitAI. CivitAI-matched files have identical SHA-256 checksums (listed below). CivitAI carries the sample videos, the version history and the comment threads; this repository exists so the weights can be pulled with a plain `resolve` URL, without an account. One **anatomy** adapter teaches H3 what a body part looks like. One **action** adapter teaches it what a body does. They are single-concept and they stack, so put them under whatever character or scene LoRA you are already running.
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