SmolVLM: Redefining small and efficient multimodal models
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2504.05299
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gg-hf
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philschmid
posted
an
update
21 days ago
Post
2570
Gemini 2.5 Pro, thinking by default! We excited launch our best Gemini model for reasoning, multimodal and coding yet! #1 on LMSYS, Humanity’s Last Exam, AIME and GPQA and more!
TL;DR:
- 💻 Best Gemini coding model yet, particularly for web development (excels on LiveCodeBench).
- 🧠 Default "Thinking" with up to 64k token output
- 🌌 1 Million multimodal input context for text, image, video, audio, and pdf
- 🛠️ Function calling, structured output, google search & code execution.
- 🏆 #1 on LMArena & sota on AIME, GPQA, Humanity's Last Exam
- 💡 Knowledge cut of January 2025
- 🤗 Available for free as Experimental in AI Studio, Gemini API & Gemini APP
- 🚀 Rate limits - Free 2 RPM 50 req/day
Try it ⬇️
https://aistudio.google.com/?model=gemini-2.5-pro-exp-03-25
TL;DR:
- 💻 Best Gemini coding model yet, particularly for web development (excels on LiveCodeBench).
- 🧠 Default "Thinking" with up to 64k token output
- 🌌 1 Million multimodal input context for text, image, video, audio, and pdf
- 🛠️ Function calling, structured output, google search & code execution.
- 🏆 #1 on LMArena & sota on AIME, GPQA, Humanity's Last Exam
- 💡 Knowledge cut of January 2025
- 🤗 Available for free as Experimental in AI Studio, Gemini API & Gemini APP
- 🚀 Rate limits - Free 2 RPM 50 req/day
Try it ⬇️
https://aistudio.google.com/?model=gemini-2.5-pro-exp-03-25
- 3 replies
clefourrier
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an
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about 1 month ago
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2178
Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
alvarobartt
posted
an
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about 2 months ago
Post
3027
🔥 Agents can do anything!
@microsoft
Research just announced the release of Magma 8B!
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
giffmana
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about 2 months ago
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12951
We did it. Kokoro TTS (v1.0) can now run 100% locally in your browser w/ WebGPU acceleration. Real-time text-to-speech without a server. ⚡️
Generate 10 seconds of speech in ~1 second for $0.
What will you build? 🔥
webml-community/kokoro-webgpu
The most difficult part was getting the model running in the first place, but the next steps are simple:
✂️ Implement sentence splitting, allowing for streamed responses
🌍 Multilingual support (only phonemization left)
Who wants to help?
Generate 10 seconds of speech in ~1 second for $0.
What will you build? 🔥
webml-community/kokoro-webgpu
The most difficult part was getting the model running in the first place, but the next steps are simple:
✂️ Implement sentence splitting, allowing for streamed responses
🌍 Multilingual support (only phonemization left)
Who wants to help?
- 11 replies
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