Lumina-DiMOO
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Explore AI models 84889
Lumina-Image-2.0
Alpha-VLLM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 ↗Lumina-mGPT-7B-768
Alpha-VLLM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 ↗clef-flash-NVFP4
alpha-x-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 ↗AliceAI-T5-35B-A0.6B-GGUF
alpha2beta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 ↗Qwen2.5-Coder-1.5B-Instruct-Q4_K_M-GGUF
alphaduriendur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 ↗bge-m3-ces-16384
alphaedge-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 ↗bge-m3-fas-32768
alphaedge-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 ↗multilingual-e5-large-nep-16384
alphaedge-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 ↗multilingual-e5-small-fra-32768
alphaedge-aiThis model is a **70.91% smaller** version of intfloat/multilingual-e5-small optimized for **French** language via vocabulary size reduction using the trimming method. This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary. - **Number of texts used for mining**: 200,000 texts - **Dataset**: lbourdois/fineweb-2-trimming
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View details ↗LYNX-ALPHA-GGUF
alphalabz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 ↗legal-chatbot-grpo-llama3.2-3b
ALPHARESZ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 ↗Hy-MT2-1.8B-SpecDraft-30B
alphaZimuth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 ↗Hy-MT2-30B-A3B-APEX-GGUF
alphaZimuthThis repository provides three APEX quantization variants optimized for different deployment scenarios, quantized by **alphaZimuth**. Compared with the original APEX quantizations, these models were calibrated with an importance matrix (`imatrix.dat`) generated from a multilingual translation-focused calibration dataset. In practical testing, the Imatrix-calibrated versions consistently provide better translation quality and output stability compared with the non-calibrated versions.
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View details ↗Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
alphaZimuthAPEX GGUF quantizations of **OS-Software/Hy-MT2-30B-A3B-uncensored-heretic**, quantized by **alphaZimuth**. This release is based directly on the existing uncensored / decensored model from **OS-Software**, rather than performing an additional uncensoring or weight-ablation process on the original Tencent model. The source model was created from **Tencent Hy-MT2-30B-A3B** using **Heretic v1.4.0+custom**, with **Arbitrary-Rank Ablation (ARA)**, a LoRA adapter, and row-norm preservation.
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View details ↗Hy-MT2-30B-A3B-Uncensored-v1-APEX-GGUF
alphaZimuth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 ↗Index-Translate-35B-A3B-preview-APEX-GGUF
alphaZimuth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 ↗c4ai-command-r-plus-GPTQ
alpindale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
alpindale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 ↗Llama-3.2-11B-Vision-Instruct
alpindale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 ↗Llama-3.2-1B
alpindale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 ↗Llama-3.2-1B-Instruct
alpindale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 ↗Llama-3.2-90B-Vision
alpindale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 ↗Llama-Guard-3-1B
alpindaleLlama Guard 3-1B is a fine-tuned Llama-3.2-1B pretrained model for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. Llama Guard 3-1B was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to lower the deployment cost of moderation system safeguard compared to its predecessors. It comes in two versions : 1B and 1B pruned and quantized, optimized for deployment on mobile devices. This repository contains two versions of Llama-Guard-3-1B, for use with transformers and with the original `llama` codebase. Once you have access to the model weights, follow the appropriate section.
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