Hugging Face Introduces Open-Source SmolVLM Vision …
10 hours ago — Hugging Face has also open-sourced the SmolVLM vision model under the Apache 2.0 license for both personal and commercial usage. Hugging Face Introduces SmolVLM. In a blog post, Hugging Face detailed the new open-source vision model. The company called the AI model “state-of-the-art” for its efficient usage of memory and fast inference.
SmolVLM - small yet mighty Vision Language Model - Hugging …
6 days ago — This blog post introduces SmolVLM, a 2B VLM, SOTA for its memory footprint. SmolVLM is small, fast, memory-efficient, and fully open-source. All model checkpoints, VLM datasets, training recipes and tools are released under the Apache 2.0 license.
Hugging Face’s SmolVLM could cut AI costs for ... - VentureBeat
5 days ago — Hugging Face launches SmolVLM, a compact and efficient vision-language AI model, offering businesses a cost-effective solution for advanced AI implementation without sacrificing performance.
Hugging Face Unveils SmolVLM, Visual Language AI For the Edge
5 days ago — AI startup Hugging Face has released an open-source family of compact visual language models named SmolVLM. With two billion parameters, it’s built for on-device inference, which the company claims outperforms similar models with comparable GPU RAM usage and token throughputs.
Hugging Face Releases SmolVLM: A 2B Parameter Vision …
6 days ago — Hugging Face recently released SmolVLM, a 2B parameter vision-language model specifically designed for on-device inference. SmolVLM outperforms other models with comparable GPU RAM usage and token throughput.
GitHub - huggingface/smollm: Everything about the SmolLM
News 📰. Introducing SmolVLM, a Vision Language Model using SmolLM2 as a language backbone 🚀; We added local_inference for running SmolLM2 on-device with MLX, MLC, Transformers.js and llama.cpp; We released the nanotron checkpoints of SmolLM2 at SmolLM2-nanotron-ckpt
Hugging Face Launches 2B Parameter Visual Language Model SmolVLM…
5 days ago — Recently, Hugging Face released SmolVLM, a 2B parameter visual language model specifically designed for on-device inference. SmolVLM outperforms other similar models in terms of GPU memory usage and token generation speed.
Hugging Face model SmolVLM requires a lot less compute
4 days ago — For SmolVLM, Hugging Face significantly modified the architecture, resulting in a model that requires 5.02 GB of RAM. For example, this is significantly less than InternVL2 2B, which requires 10.52 GB of memory. This more efficient approach makes SmolVLM suitable for on-device applications, with the model continuing to deliver strong performance.
Hugging Face’s SmolVLM could cut AI costs for businesses by a …
3 days ago — Hugging Face introduces SmolVLM, a new compact and efficient vision-language AI model designed to provide businesses with a cost-effective solution for
@merve on Hugging Face: "Small yet mighty! We are releasing SmolVLM …
We are releasing SmolVLM: a new 2B small vision language made for on-device use, fine-tunable on consumer GPU, immensely memory efficient 🤠 We release three checkpoints under Apache 2.0: SmolVLM-Instruct, SmolVLM-Synthetic and SmolVLM-Base HuggingFaceTB/smolvlm-6740bd584b2dcbf51ecb1f39
- Some results have been removed