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⚠️ This is a modified version of Florence 2 that modifies the custom `modeling_florence2.py` file to remove the need for installing `flash-attn` package (by hijacking the flash-attn methods and replacing with regular attention). It probably has impact in performance. This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

Input Text · Image→ Output Text
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A `trust_remote_code` packaging of `bosonai/higgs-audio-v3-tts-4b` that loads with plain 🤗 transformers (no SGLang). The weights are the original checkpoint, copied unchanged; only a small `modeling_*.py` / `configuration_*.py` pair and an `auto_map` were added. The model is a standard **Qwen3-4B** backbone plus a fused multi-codebook audio embedding/head. Reference-audio encoding and waveform decoding use the transformers-native `bosonai/higgs-audio-v2-tokenizer` (`higgs_audio_v2_tokenizer`), loaded automatically on first use. - Generation uses Higgs' delay pattern across 8 codebooks (vocab 1026, incl. BOC/EOC specials); de-delay + decode are handled internally. - The codec runs in fp32 (decode is unstable in bf16); the LM backbone runs in the dtype you load it in (bf16 recommended). - License: research/non-commercial, inherited from the upstream checkpoint — see `LICENSE`.

Input Text→ Output Audio
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MiniMax-H3-Pruned

multimodalart

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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