SmolVLM - small yet mighty Vision Language Model
•
62
Andres Marafioti
andito
AI & ML interests
Multimodal models, VLM and TTS
Recent Activity
updated
a dataset
about 6 hours ago
huggingface/documentation-images
New activity
about 6 hours ago
HuggingFaceTB/SmolVLM-Instruct:ValueError: `resolution_max_side` cannot be larger than `max_image_size` with N=5
Articles
Organizations
Posts
3
Post
319
Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.
- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! 🤯
- Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! 🚀
- SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU!
- SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!
Check out more!
Demo: HuggingFaceTB/SmolVLM
Blog: https://huggingface.co/blog/smolvlm
Model: HuggingFaceTB/SmolVLM-Instruct
Fine-tuning script: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
- SmolVLM generates tokens 7.5 to 16 times faster than Qwen2-VL! 🤯
- Other models at this size crash a laptop, but SmolVLM comfortably generates 17 tokens/sec on a macbook! 🚀
- SmolVLM can be fine-tuned on a Google collab! Or process millions of documents with a consumer GPU!
- SmolVLM even outperforms larger models in video benchmarks, despite not even being trained on videos!
Check out more!
Demo: HuggingFaceTB/SmolVLM
Blog: https://huggingface.co/blog/smolvlm
Model: HuggingFaceTB/SmolVLM-Instruct
Fine-tuning script: https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
Post
1065
Hugging face presents FineVideo 🎥! Unlocking the next generation of Video understanding 🚀
🤯3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
🔥
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. ⚡️
Explore the videos: HuggingFaceFV/FineVideo-Explorer
🤯3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
🔥
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. ⚡️
Explore the videos: HuggingFaceFV/FineVideo-Explorer
