141–150 of 278 posts
The models themselves are showing up on Hugging Face here: https://huggingface.co/prism-ml/models I've tried a couple in LM Studio - the GGUF one and the MLX one - but neither worked there. Anyone else get them to work? Might be that LM Studio needs to upgrade their llama.cpp or MLX engines first.
What's the hiring space and business strategy around all of these smaller AI labs? Its really cool that people like these guys get paid to optimize models and give them out for free (open source). Do a lot of these labs have forward deployed engineers doing integrations with customers who want local models? Is there a general shift towards the local model crowd?
What I most want to see it compared to is Gemma 4 12B in the 4-bit QAT version. It's barely bigger than this at just under 7GB, so it also runs on just about any modern device and is remarkably smart for its size. It's an excellent tool user, crazy good vision for its size. I'm still trying to wrap my head around how much is lost with each step down in resolution, but the QAT versions from Google seem to prove the an…
Based on their numbers and cross referencing with the Gemma numbers, this model crushes Gemma 4 12b on math and coding, is slightly worse on knowledge and tool calling, and is significantly worse on vision tasks.
Earlier quoted context omitted.
Based on their numbers and cross referencing with the Gemma numbers, this model crushes Gemma 4 12b on math and coding, is slightly worse on knowledge and tool calling, and is significantly worse on vision tasks.
I think this is where leveraging classifier models will become important. The frontier LLM models do "everything", while we've known for a while that to truly scale this we will need to distill models into their individual functions. I don't see this as necessarily a bad thing and hope more is done in this space. Very promising.
maybe its nitpicking here but the demo shows them asking the model what to cook and its recipie sounds like it wouldn't be very good and also it totally gets the macronutrients wrong. 25g protein for "spaghetti, carrots, peppers, garlic and herbs"?
Even the lowest quality one has 12g/100g.
What I most want to see it compared to is Gemma 4 12B in the 4-bit QAT version. It's barely bigger than this at just under 7GB, so it also runs on just about any modern device and is remarkably smart for its size. It's an excellent tool user, crazy good vision for its size. I'm still trying to wrap my head around how much is lost with each step down in resolution, but the QAT versions from Google seem to prove the an…
Based on their numbers and cross referencing with the Gemma numbers, this model crushes Gemma 4 12b on math and coding, is slightly worse on knowledge and tool calling, and is significantly worse on vision tasks.
What I most want to see it compared to is Gemma 4 12B in the 4-bit QAT version. It's barely bigger than this at just under 7GB, so it also runs on just about any modern device and is remarkably smart for its size. It's an excellent tool user, crazy good vision for its size. I'm still trying to wrap my head around how much is lost with each step down in resolution, but the QAT versions from Google seem to prove the an…
maybe its nitpicking here but the demo shows them asking the model what to cook and its recipie sounds like it wouldn't be very good and also it totally gets the macronutrients wrong. 25g protein for "spaghetti, carrots, peppers, garlic and herbs"?
Good spaghetti has 15% protein, 15g per 100g. Even the lowest quality one has 12g/100g.