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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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SmolVLM-Synthetic

HuggingFaceTB

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Visual QA
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SmolVLM2-2.2B-Base

HuggingFaceTB

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SmolVLM2-2.2B is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 5.2GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. - **Developed by:** Hugging Face 🤗 - **Model type:** Multi-modal model (image/multi-image/video/text) - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Architecture:** Based on Idefics3 (see technical summary) - **Demo:** Video Highlight Generator - **Blog:** Blog post

Visual QA
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SmolVLM2-256M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.38GB of GPU RAM for video inference. This efficiency makes it particularly well-suited for on-device applications that require specific domain fine-tuning and computational resources may be limited. - **Developed by:** Hugging Face 🤗 - **Model type:** Multi-modal model (image/multi-image/video/text) - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Architecture:** Based on Idefics3 (see technical summary) - **Demo:** Video Highlight Generator - **Blog:** Blog post

Visual QA
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SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. - **Developed by:** Hugging Face 🤗 - **Model type:** Multi-modal model (image/multi-image/video/text) - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Architecture:** Based on Idefics3 (see technical summary) - **Demo:** Video Highlight Generator - **Blog:** Blog post

Text generationOpen weights
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This is a classifier for scoring the educational value of code files in The Stack v2 dataset, it is a finetuned version of bigcode/starencoder with a classification head on code files annotated by Llama3.1-70B-Instruct. We use this classifier for building Stack-Edu dataset used for training SmolLM2, see paper. Each classifier is trained on one programming language. While the classifier performs well in distinguishing high-quality code in its target language (JS in this case), there are some limitations: - Scope: The model's performance might change for other datasets, in particular for out of distribution samples. The classifier's context is 1024 tokens, which might not be sufficient to assess the quality of some long code files. - Bias: The model's performance is dependent on the quality and representativeness of the training data and the LLM used for the annotation. Biases in both can affect the classifier's judgments. It might overfit to thoroughly commented code. - Context: The classifier evaluates individual code files without considering broader context, which might impact its effectiveness in certain scenarios.

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smolvla_libero

HuggingFaceVLA

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.

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