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nanoVLM-sft_debug
Afarut/nanoVLM-sft_debug
nanoVLM-sft_debug is a image-text-to-text model from Afarut. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for nanovlm. The card lists the license as mit.
nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines o…
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228M
5.5 GB on disk
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.safetensors912 MB · 100%
At a glance
- Task
- Image-Text-to-Text
- Library
- nanovlm
- License
- mit
- Access
- Public
- Created
- Mar 4, 2026
- Updated
- Mar 4, 2026
- SHA
- be6834bd
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Model card
From the Hugging Face model README
nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
Usage:
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code:
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("Afarut/nanoVLM-sft_debug")
- Task
- Image-Text-to-Text
- Library
- nanovlm
- License
- mit
- Created
- Mar 4, 2026
- Updated
- Mar 4, 2026