smolvlm2 #2690

mfarre · 2025-02-20T08:17:05Z

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merveenoyan

otherwise looks good!

merveenoyan · 2025-02-20T09:25:10Z

smolvlm2.md

+
+To demonstrate our vision in small video models, we've built three practical applications that showcase the versatility of these models.
+
+### iPhone Video Understanding


we should move the use cases right after section SmolVLM2 2.2B: Our New Star Player for Vision and Video, and put ToC right here imo for better readability

Table of Contents

SmolVLM2 Family: Bringing Video Understanding to Every Device

TL;DR: SmolVLM can now watch 📺 with even better visual understanding

Table of Contents

Technical Details

SmolVLM2 2.2B: Our New Star Player for Vision and Video

Going Even Smaller: Meet the 500M and 256M Video Models

[Suite of SmolVLM2 Apps]

iPhone Video Understanding

Video Highlight Generator

VLC media player integration

Using SmolVLM2 and Fine-tuning it with Transformers and MLX

Transformers

Video Inference

Multiple Image Inference

Interleaving Image, Text and Video

Inference with MLX

Fine-tuning SmolVLM2

Read More

also no need to make collection a subsection, we can just mention in TLDR

I like the idea of diving in into the applications. Do we really need a ToC for this post? (Just unsure)

pcuenca · 2025-02-20T09:29:12Z

_blog.yml

  thumbnail: /blog/assets/paligemma2/thumbnail.png
  author: ariG23498
-  date: Feb 19, 2024
+  date: Feb 19, 2025


merveenoyan · 2025-02-20T09:29:35Z

smolvlm2.md

+  - [Technical Details](#technical-details)
+    - [SmolVLM2 2.2B: Our New Star Player for Vision and Video](#smolvlm2-22b-our-new-star-player-for-vision-and-video)
+    - [Going Even Smaller: Meet the 500M and 256M Video Models](#going-even-smaller-meet-the-500m-and-256m-video-models)
+  - [Using SomlVLM2 and Fine-tuning it with Transformers and MLX](#using-somlvlm2-and-fine-tuning-it-with-transformers-and-mlx)


Suggested change

- [Using SomlVLM2 and Fine-tuning it with Transformers and MLX](#using-somlvlm2-and-fine-tuning-it-with-transformers-and-mlx)

- [Using SmolVLM2 and Fine-tuning it with Transformers and MLX](#using-smolvlm2-and-fine-tuning-it-with-transformers-and-mlx)

merveenoyan · 2025-02-20T09:29:46Z

smolvlm2.md

+
+
+
+## Using SomlVLM2 and Fine-tuning it with Transformers and MLX


Suggested change

## Using SomlVLM2 and Fine-tuning it with Transformers and MLX

## Using SmolVLM2 and Fine-tuning it with Transformers and MLX

merveenoyan

add call to action

merveenoyan · 2025-02-20T09:31:07Z

smolvlm2.md

+
+
+
+## Read More


Suggested change

## Read More

## Read More

We are looking forward to see all the things you'll build with SmolVLM2!

If you'd like to learn more about SmolVLM family of models, feel free to read the following.

merveenoyan · 2025-02-20T09:31:52Z

smolvlm2.md

+```
+
+### Fine-tuning SmolVLM2
+


I will add the video FT script to SmolLM repo today and we can link to my existing notebook for image FT to smol-vision

pcuenca

Awesome 🔥 Made a few optional suggestions.

pcuenca · 2025-02-20T09:29:44Z

_blog.yml

+  author: mfarre
+  thumbnail: /blog/assets/smolvlm2/banner.png
+  date: Feb 20, 2025
+  tags:


don't we have one for vlm? Perhaps we should :)

we actually have one, added

pcuenca · 2025-02-20T09:30:30Z

_blog.yml

+    - vision
+
+- local: smolvlm2
+  title: "SmolVLM2 Family: Bringing Video Understanding to Every Device"


Suggested change

title: "SmolVLM2 Family: Bringing Video Understanding to Every Device"

title: "SmolVLM2: Bringing Video Understanding to Every Device"

Maybe more concise? Not sure we need the "family" term here, but your call!

pcuenca · 2025-02-20T09:30:56Z

smolvlm2.md

@@ -0,0 +1,315 @@
+---
+title: "SmolVLM2 Family: Bringing Video Understanding to Every Device" 


Same (if you agree)

pcuenca · 2025-02-20T09:32:47Z

smolvlm2.md

+- user: orrzohar
+  guest: true
+  org: Stanford
+- user: mfarre


The main author should be inferred from _blog.yml, but there was a bug and it was taken from the first author in this list. I think it has been fixed recently, but I'd recommend to put mfarre first just in case.

Suggested change

- user: orrzohar

guest: true

org: Stanford

- user: mfarre

- user: mfarre

- user: orrzohar

guest: true

org: Stanford

set Orr as author

pcuenca · 2025-02-20T09:34:31Z

smolvlm2.md

+
+SmolVLM2 represents a fundamental shift in how we think about video understanding - moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.
+
+We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready from day zero.


Suggested change

We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready from day zero.

We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready (Python _and_ Swift APIs) from day zero.

I still need to cleanup some code and submit a few PRs to the repos 😅 Hope we can get it done in time.

pcuenca · 2025-02-20T10:09:48Z

smolvlm2.md

+    },
+]
+
+# infer like above


I'd show a complete snippet so people can copy/paste.

added inference code

smolvlm2.md

pcuenca · 2025-02-20T10:10:35Z

smolvlm2.md

+
+
+


Still missing cc @merveenoyan

pcuenca · 2025-02-20T10:11:55Z

smolvlm2.md

+## Read More
+[SmolVLM2 - Collection with Models and Demos](https://huggingface.co/collections/HuggingFaceTB/smolvlm2-smallest-video-lm-ever-67ab6b5e84bf8aaa60cb17c7)
+[SmolVLM - small yet mighty Vision Language Model](https://huggingface.co/blog/smolvlm)


Maybe replace with a conclusion still. We can still link to the collection there, but not to the previous blog post imo (we can do it elsewhere in the post), since this would not be a more in-depth reading.

pcuenca · 2025-02-20T10:13:22Z

smolvlm2.md

+
+### Transformers
+
+There are two ways to infer SmolVLM2 models, one is through a chat template and the other one gives you more control by passing in media through the processor.


Suggested change

There are two ways to infer SmolVLM2 models, one is through a chat template and the other one gives you more control by passing in media through the processor.

The easiest way to run inference with the SmolVLM2 models is through the conversational API – applying the chat template takes care of preparing all inputs automatically.

Removing the processor use because we are not showing it later.

Co-authored-by: Merve Noyan <merveenoyan@gmail.com>

smolvlm2.md

pcuenca · 2025-02-20T12:00:50Z

smolvlm2.md

+
+
+
+## Using SmolVLM2 and Fine-tuning it with Transformers and MLX


Suggested change

## Using SmolVLM2 and Fine-tuning it with Transformers and MLX

## Using SmolVLM2 with Transformers and MLX

maybe?

xenova

Super cool! Just some suggestions & nits.

I could also add a Transformers.js example code section soon (requires installing from main at the moment, but I will put out a release for it soon).

xenova · 2025-02-20T11:59:20Z

smolvlm2.md

+
+## TL;DR: SmolVLM can now watch 📺 with even better visual understanding
+
+SmolVLM2 represents a fundamental shift in how we think about video understanding - moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.


Suggested change

SmolVLM2 represents a fundamental shift in how we think about video understanding - moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.

SmolVLM2 represents a fundamental shift in how we think about video understanding — moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.

xenova · 2025-02-20T12:03:33Z

smolvlm2.md

+We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready (Python _and_ Swift APIs) from day zero.
+


Suggested change

We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready (Python _and_ Swift APIs) from day zero.

We are releasing models in three sizes ([2.2B](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct), [500M](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) and [256M](https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct)), MLX (Python _and_ Swift APIs) and ONNX ready from day zero.

Added links for people to easily navigate to the models. Could replace with a collection link though.

Maybe not needed to mention ONNX. Feel free to remove.

Btw, is it intentional that the naming is different for each model?
e.g., https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct vs. https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruc

xenova · 2025-02-20T12:11:08Z

smolvlm2.md

+We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready (Python _and_ Swift APIs) from day zero.
+
+
+To demonstrate our vision in small video models, we've built three practical applications that showcase the versatility of these models.


Suggested change

To demonstrate our vision in small video models, we've built three practical applications that showcase the versatility of these models.

To demonstrate our vision for small video models, we've built three practical applications that showcase the versatility of these models.

xenova · 2025-02-20T12:16:33Z

smolvlm2.md

+</center>
+</td>
+<td valign="top" style="border: none;">
+We've created an iPhone app running SmolVLM2 completely locally. Using our 500M model, users can analyze and understand video content directly on their device - no cloud required. Interested in building iPhone video processing apps with AI models running locally? We're releasing it very soon - <a href="https://huggingface.co/datasets/HuggingFaceTB/smolvlm2-iphone-waitlist" target="_blank">fill this form to test and build with us!</a>


Suggested change

We've created an iPhone app running SmolVLM2 completely locally. Using our 500M model, users can analyze and understand video content directly on their device - no cloud required. Interested in building iPhone video processing apps with AI models running locally? We're releasing it very soon - <a href="https://huggingface.co/datasets/HuggingFaceTB/smolvlm2-iphone-waitlist" target="_blank">fill this form to test and build with us!</a>

We've created an iPhone app that runs SmolVLM2 completely locally. Using our 500M model, users can analyze and understand video content directly on their device — no cloud required. Interested in building iPhone video processing apps with AI models running locally? We're releasing it very soon — <a href="https://huggingface.co/datasets/HuggingFaceTB/smolvlm2-iphone-waitlist" target="_blank">fill in this form to test and build with us!</a>

xenova · 2025-02-20T12:17:40Z

smolvlm2.md

+</center>
+</td>
+<td valign="top" style="border: none;">
+Available as a Hugging Face Space, this application takes long-form videos (1+ hours) and automatically extracts the most significant moments. We've tested it extensively with soccer matches and other lengthy events, making it a powerful tool for content summarization. <a href="https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator" target="_blank">Try it yourself in our demo space.</a>


Suggested change

Available as a Hugging Face Space, this application takes long-form videos (1+ hours) and automatically extracts the most significant moments. We've tested it extensively with soccer matches and other lengthy events, making it a powerful tool for content summarization. <a href="https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator" target="_blank">Try it yourself in our demo space.</a>

Available as a Hugging Face Space, this application takes long-form videos (1+ hours) and automatically extracts the most significant moments. We've tested it extensively with soccer matches and other lengthy events, making it a powerful tool for content summarization. <a href="https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator" target="_blank">Try it yourself with our demo space.</a>

xenova · 2025-02-20T12:21:38Z

smolvlm2.md

+
+We are introducing three new models with 256M, 500M and 2.2B parameters. The 2.2B model is the go-to choice for vision and video tasks, while the 500M and 256M models represent **the smallest video language models ever released**.
+
+While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.


Suggested change

While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.

While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families in the 2B range and we lead the pack in the even smaller space.

Maybe also add a link to Video-MME on HF?

Ah I see the footnote. Maybe add a link to it here.

xenova · 2025-02-20T12:22:42Z

smolvlm2.md

+
+While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2-videomme2.png" width="50%" alt="SmolVLM2 Performance">


Indeed, and it seems to only be for some text.

xenova · 2025-02-20T12:25:00Z

smolvlm2.md

+
+
+
+When it comes to video tasks, 2.2B is a good bang for the buck. Across the different scientific benchmarks where we evaluated it we want to highlight its performance on Video-MME where it outperforms all existing 2B models. 


Suggested change

When it comes to video tasks, 2.2B is a good bang for the buck. Across the different scientific benchmarks where we evaluated it we want to highlight its performance on Video-MME where it outperforms all existing 2B models.

When it comes to video tasks, 2.2B is a good bang for the buck. Across the various scientific benchmarks we evaluated it on, we want to highlight its performance on Video-MME where it outperforms all existing 2B models.

Maybe a better way to phrase this too. Original wording confused me.

xenova · 2025-02-20T12:31:55Z

smolvlm2.md

+
+#### Video Inference
+
+You can pass videos through a chat template by passing in {“type”: “video”, “path”:{video_path}”. See below complete example. 


Suggested change

You can pass videos through a chat template by passing in {“type”: “video”, “path”:{video_path}”. See below complete example.

You can pass videos through a chat template by passing in `{"type": "video", "path": {video_path}`. See below for a complete example.

xenova · 2025-02-20T12:33:01Z

smolvlm2.md

+
+#### Interleaving Image, Text and Video
+
+You can interleave image, video and text together by passing in `<image>` and `<video>` tokens inside text, cutting text through and inserting image lines in between.


Probably fine to remove this section due to performance, and also it's a pretty niche use-case imo.

merveenoyan · 2025-02-20T13:06:44Z

smolvlm2.md

+
+
+## Read More
+


Suggested change

We would like to thank Raushan Turganbay, Arthur Zucker and Pablo Montalvo Leroux for their contribution of the model to transformers.

(I just created it 😂)

merveenoyan

added notebook

merveenoyan · 2025-02-20T13:32:14Z

smolvlm2.md

+
+### Fine-tuning SmolVLM2
+
+


Suggested change

You can fine-tune SmolVLM2 on videos using transformers 🤗

We have fine-tuned 500M variant in Colab on video-caption pairs in [VideoFeedback](https://huggingface.co/datasets/TIGER-Lab/VideoFeedback) dataset for demonstration purposes. Since 500M variant is small, it's better to apply full fine-tuning instead of QLoRA or LoRA, meanwhile you can try to apply QLoRA on 2.2B variant. You can find the fine-tuning notebook [here](https://github.com/huggingface/smollm/blob/main/vision/finetuning/SmolVLM2_Video_FT.ipynb).

smolvlm2

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mfarre requested review from merveenoyan and pcuenca February 20, 2025 08:17

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_blog.yml

thumbnail: /blog/assets/paligemma2/thumbnail.png

author: ariG23498

date: Feb 19, 2024

date: Feb 19, 2025

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Thanks!

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mfarre and others added 2 commits February 20, 2025 11:56

Update smolvlm2.md

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Co-authored-by: Merve Noyan <merveenoyan@gmail.com>

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MLX smolvlm

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		To demonstrate our vision in small video models, we've built three practical applications that showcase the versatility of these models.

		### iPhone Video Understanding

	- [Using SomlVLM2 and Fine-tuning it with Transformers and MLX](#using-somlvlm2-and-fine-tuning-it-with-transformers-and-mlx)
	- [Using SmolVLM2 and Fine-tuning it with Transformers and MLX](#using-smolvlm2-and-fine-tuning-it-with-transformers-and-mlx)




		## Using SomlVLM2 and Fine-tuning it with Transformers and MLX

	## Using SomlVLM2 and Fine-tuning it with Transformers and MLX
	## Using SmolVLM2 and Fine-tuning it with Transformers and MLX

	title: "SmolVLM2 Family: Bringing Video Understanding to Every Device"
	title: "SmolVLM2: Bringing Video Understanding to Every Device"

		@@ -0,0 +1,315 @@
		---
		title: "SmolVLM2 Family: Bringing Video Understanding to Every Device"


		SmolVLM2 represents a fundamental shift in how we think about video understanding - moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.

		We are releasing models in three sizes (2.2B, 500M and 256M), MLX ready from day zero.


		### Transformers

		There are two ways to infer SmolVLM2 models, one is through a chat template and the other one gives you more control by passing in media through the processor.

	There are two ways to infer SmolVLM2 models, one is through a chat template and the other one gives you more control by passing in media through the processor.
	The easiest way to run inference with the SmolVLM2 models is through the conversational API – applying the chat template takes care of preparing all inputs automatically.

	## Using SmolVLM2 and Fine-tuning it with Transformers and MLX
	## Using SmolVLM2 with Transformers and MLX


		## TL;DR: SmolVLM can now watch 📺 with even better visual understanding

		SmolVLM2 represents a fundamental shift in how we think about video understanding - moving from massive models that require substantial computing resources to efficient models that can run anywhere. Our goal is simple: make video understanding accessible across all devices and use cases, from phones to servers.

	To demonstrate our vision in small video models, we've built three practical applications that showcase the versatility of these models.
	To demonstrate our vision for small video models, we've built three practical applications that showcase the versatility of these models.


		We are introducing three new models with 256M, 500M and 2.2B parameters. The 2.2B model is the go-to choice for vision and video tasks, while the 500M and 256M models represent the smallest video language models ever released.

		While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.


		While they're small in size, they outperform any existing models per memory consumption. Looking at Video-MME (the go-to scientific benchmark in video), SmolVLM2 joins frontier model families on the 2B range and we lead the pack in the even smaller space.

		<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2-videomme2.png" width="50%" alt="SmolVLM2 Performance">




		When it comes to video tasks, 2.2B is a good bang for the buck. Across the different scientific benchmarks where we evaluated it we want to highlight its performance on Video-MME where it outperforms all existing 2B models.


		#### Video Inference

		You can pass videos through a chat template by passing in {“type”: “video”, “path”:{video_path}”. See below complete example.


		#### Interleaving Image, Text and Video

		You can interleave image, video and text together by passing in `<image>` and `<video>` tokens inside text, cutting text through and inserting image lines in between.


	We would like to thank Raushan Turganbay, Arthur Zucker and Pablo Montalvo Leroux for their contribution of the model to transformers.


	You can fine-tune SmolVLM2 on videos using transformers 🤗

	We have fine-tuned 500M variant in Colab on video-caption pairs in [VideoFeedback](https://huggingface.co/datasets/TIGER-Lab/VideoFeedback) dataset for demonstration purposes. Since 500M variant is small, it's better to apply full fine-tuning instead of QLoRA or LoRA, meanwhile you can try to apply QLoRA on 2.2B variant. You can find the fine-tuning notebook [here](https://github.com/huggingface/smollm/blob/main/vision/finetuning/SmolVLM2_Video_FT.ipynb).

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