Instructions to use sthui/SimpleSeg-Kimi-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sthui/SimpleSeg-Kimi-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sthui/SimpleSeg-Kimi-VL", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sthui/SimpleSeg-Kimi-VL", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Introduction
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> [!Note]
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> This is [Kimi-VL](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905) version of SimpleSeg, an architecture with 16B-A3B paramters.
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We present **SimpleSeg**, **a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception**.
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## Introduction
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> [!Note]
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> This is [Kimi-VL](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905) version of SimpleSeg, an MoE architecture with 16B-A3B paramters.
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We present **SimpleSeg**, **a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception**.
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