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#SmallModels

Dr. Thompsonrogt_x1997
2025-06-21

🚀 Small model, massive impact! Meet Juniper — the 2B-parameter AI that’s outperforming giants like GPT-4o in function calling precision. Ready to rethink what size means in AI? Dive in and discover the future of lean, local LLMs 💡
👉 medium.com/@rogt.x1997/juniper

medium.com/@rogt.x1997/juniper

Dr. Thompsonrogt_x1997
2025-06-01

Hook:
💡 What if the secret to faster, cheaper, smarter AI isn’t going bigger—but smaller?

Message:
I cut 88% of my AI inference costs by switching to Small Language Models (SLMs).
This article breaks down how compact models like Phi-3 and Gemma are beating giants like GPT-4 in cost, speed, and privacy.

🚀 Ready to rethink your GenAI strategy?

🔗 medium.com/@rogt.x1997/8-reaso


medium.com/@rogt.x1997/8-reaso

Nicole Hennignic221.bsky.social@bsky.brid.gy
2025-02-27

Microsoft’s new Phi-4 AI models pack big performance in small packages https://venturebeat.com/ai/microsofts-new-phi-4-ai-models-pack-big-performance-in-small-packages/ #AI #SmallModels

Text Shot: Microsoft has introduced a new class of highly efficient AI models that process text, images, and speech simultaneously while requiring significantly less computing power than existing systems. The new Phi-4 models, released today, represent a breakthrough in the development of small language models (SLMs) that deliver capabilities previously reserved for much larger AI systems.
Nicole Hennignic221@techhub.social
2025-02-27

Microsoft’s new Phi-4 AI models pack big performance in small packages venturebeat.com/ai/microsofts- #AI #SmallModels

Text Shot: Microsoft has introduced a new class of highly efficient AI models that process text, images, and speech simultaneously while requiring significantly less computing power than existing systems. The new Phi-4 models, released today, represent a breakthrough in the development of small language models (SLMs) that deliver capabilities previously reserved for much larger AI systems.
Nicole Hennignic221.bsky.social@bsky.brid.gy
2025-01-25

Hugging Face shrinks AI vision models to phone-friendly size, slashing computing costs https://venturebeat.com/ai/hugging-face-shrinks-ai-vision-models-to-phone-friendly-size-slashing-computing-costs/ #AI #SmallModels #EnergyEfficiency

Text Shot: The breakthrough challenges conventional wisdom about the relationship between model size and capability. While many researchers have assumed that larger models were necessary for advanced vision-language tasks, SmolVLM demonstrates that smaller, more efficient architectures can achieve similar results. The 500M parameter version achieves 90% of the performance of its 2.2B parameter sibling on key benchmarks. Rather than suggesting an efficiency plateau, Marafioti sees these results as evidence of untapped potential: “Until today, the standard was to release VLMs starting at 2B parameters; we thought that smaller models were not useful. We are proving that, in fact, models at 1/10 of the size can be extremely useful for businesses.” This development arrives amid growing concerns about AI’s environmental impact and computing costs. By dramatically reducing the resources required for vision-language AI, Hugging Face’s innovation could help address both issues while making…
Nicole Hennignic221@techhub.social
2025-01-25

Hugging Face shrinks AI vision models to phone-friendly size, slashing computing costs venturebeat.com/ai/hugging-fac #AI #SmallModels #EnergyEfficiency

Text Shot: The breakthrough challenges conventional wisdom about the relationship between model size and capability. While many researchers have assumed that larger models were necessary for advanced vision-language tasks, SmolVLM demonstrates that smaller, more efficient architectures can achieve similar results. The 500M parameter version achieves 90% of the performance of its 2.2B parameter sibling on key benchmarks. Rather than suggesting an efficiency plateau, Marafioti sees these results as evidence of untapped potential: “Until today, the standard was to release VLMs starting at 2B parameters; we thought that smaller models were not useful. We are proving that, in fact, models at 1/10 of the size can be extremely useful for businesses.” This development arrives amid growing concerns about AI’s environmental impact and computing costs. By dramatically reducing the resources required for vision-language AI, Hugging Face’s innovation could help address both issues while making…
chrischris@rtl.chrisadams.me.uk
2024-08-22

As I end up reading more around AI, I came across this snippet from a recent post by Sayah Kapor, which initially felt really counter intuitive:

Paradoxically, smaller models require more training to reach the same level of performance. So the downward pressure on model size is putting upward pressure on training compute. In effect, developers are trading off training cost and inference cost.

Source: AI scaling myths by Sayash Kapoormo

I don’t really have a complete mental model formed for training, but it’s not a million miles away from the make it work, make it right, make it fast mantra from Kent Beck.

Here, While they are obviously different things, there is often enough overlap between the idea of making something fast and making something efficient to feel like sort of can be applied.

If the last few years have been about making it work (with admittedly mixed process on making it right…), then it makes sense that this wave of small models could be interpreted as the make it fast stage of development.

Anyway.

https://rtl.chrisadams.me.uk/2024/08/til-training-small-models-can-be-more-energy-intensive-than-training-large-models/

#AI #smallModels #training

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