51–60 of 224 posts
llama3 narrowly beats arctic at SQL generation (80.2 vs 79.0) and Mixtral 8x22B scored 79.2. You'd think SQL would be the one thing they'd be sure to smoke other models on. 0 - https://www.snowflake.com/blog/arctic-open-efficient-foundat...
Yeah but that's a 70B model. You can see on the Inference Efficiency chart that it takes more than 3x as much compute to run it compared to this one.
It appears to have limited guardrails. I got it to generate some risqué story and it also told me how to trade onion futures, which is illegal in the US.
Wow, 128 experts in a single model. That's a lot more than everyone else. The Snowflake team has a blog post explaining why they did that: https://www.snowflake.com/blog/arctic-open-efficient-foundat... But the most interesting aspect about this, for me, is that every tech company seems to be coming out with a free open model claiming to be better than the others at this thing or that thing. The number of choices is…
Far fewer than 600,000 of those are pretrained. Most are finetuned which is much easier. You can finetune a 7B model on gamer cards. There is basically the big guys that everyone's heard of (google, meta, microsoft/openAI, and anthropic) and then a handful of smaller players who are training foundation models mostly so that they can prove to VCs that they are capable of doing so -- to acquire more funding/access to c…
Snowflake and the like are training and releasing new models because they intend to integrate the AI into their existing product down the line. Why not use and fine-tune an existing model? Their in-grown model maybe better suited for their product. This can also fail like Bloomberg's financial model being inferior to GPT-4, but these companies have to try.
Let’s stop using terms like open source falsely. The model isn’t open source, it is open weights. It’s good that the license for the weights is Apache, but for this model to be “truly open” they must release training data and source code under an OSI approved license. Otherwise it’s just misleading marketing. So far it seems like Snowflake will release some blog posts and “cookbooks”, whatever that means, but not act…
Thankfully, thankfully, this sort of stuff isn’t decided based on the personal reckoning of someone on Hacker News. Whether or not training data needs to be open source in order for the resulting model to be open source is, at the very least, up for debate. And that’s a charitable interpretation. This is quite clearly instead your view based on your own personal philosophy. Software licenses are legal instruments, no…
The GPL says it straight up:
> The “source code” for a work means the preferred form of the work for making modifications to it
Clearly just weights don't qualify, just like C run through an obfuscator would not count.
llama3 narrowly beats arctic at SQL generation (80.2 vs 79.0) and Mixtral 8x22B scored 79.2. You'd think SQL would be the one thing they'd be sure to smoke other models on. 0 - https://www.snowflake.com/blog/arctic-open-efficient-foundat...
Yeah but that's a 70B model. You can see on the Inference Efficiency chart that it takes more than 3x as much compute to run it compared to this one.
Or they simply consider inference efficiency as latency
Wow, 128 experts in a single model. That's a lot more than everyone else. The Snowflake team has a blog post explaining why they did that: https://www.snowflake.com/blog/arctic-open-efficient-foundat... But the most interesting aspect about this, for me, is that every tech company seems to be coming out with a free open model claiming to be better than the others at this thing or that thing. The number of choices is…
Earlier quoted context omitted.
Far fewer than 600,000 of those are pretrained. Most are finetuned which is much easier. You can finetune a 7B model on gamer cards. There is basically the big guys that everyone's heard of (google, meta, microsoft/openAI, and anthropic) and then a handful of smaller players who are training foundation models mostly so that they can prove to VCs that they are capable of doing so -- to acquire more funding/access to c…
> an exercise of pure marketing Yes. Great choice of words. A lot of non -frontier models look like "an exercise of pure marketing" to me. Still, I fail to see the rationale for telling the world, "Look at us! We can do it too!"
Earlier quoted context omitted.
>One estimate I saw was that training GPT3 released 500 tons of CO2 back in 2020 So absolute nothing in the grand scheme of things?
That's the amount that would be released by burning 50,000 gallons of gas, which is about that ten typical cars will burn throughout their entire lifespan. Done once, I agree, that's very little. But if each of those 600,000 other models used that much (or even a tenth that much), then that now becomes impactful. Releasing 500 tons of CO2 600,000 times over would amount to about 1% of all human global annual emission…
And those 600k models are mostly fine-tunes. If running your 4090 at home is too much then we're going to have to get rid of the gamers.
This CO2 objection is such an innumerate objection. Just making 100 cars already is more than making one of these LLMs from scratch. A finetune is so cheap in comparison.
In fact, I bet if you asked most LLM companies they'd gladly support a universal carbon tax with even dividend based on emissions and then you'd see who's actually emitting.
Let’s stop using terms like open source falsely. The model isn’t open source, it is open weights. It’s good that the license for the weights is Apache, but for this model to be “truly open” they must release training data and source code under an OSI approved license. Otherwise it’s just misleading marketing. So far it seems like Snowflake will release some blog posts and “cookbooks”, whatever that means, but not act…
You have a weird definition of open source. OS software developers don't release the books they have read or the tools they've used to write code. This is fully 100% OSI compliant source code with an approved license (Apache 2.0). You are not entitled to anything more than this.