Ember-1

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• Cut my teeth on Ember back in the 1.x days. The learning curve was steep, but the productivity once you got it was immense.
  by Mbarley
• Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - https://huggingface.co/ukisai
  by Arcuru
• What am I missing here? I think of fireworks as an inference provider serving open weights model. The value that they primarily provide to customers is that (i) they improve reliability by balancing across a bunch of clouds/neoclouds, (ii) they get better pricing by buying capacity in bulk, and (iii) they reduce operational costs. So far so good.

  I can also see the argument for providing a post-training service from a customer acquisition perspective: "hey, we can fine-tune this open weights model, so it both gives better/more predictable results than OpenAI/Anthropic and also is cheaper. And btw, once we've won your business, please run this model on our infra."

  But what I'm struggling to understand is fireworks spending a bunch of money (on salaries and compute) releasing a frontier model that is going to rapidly fall behind the frontier. Is this "just" advertising for them, both for customers and also for hiring? Or are they actually trying to stay on the frontier? If so, to what end?

  by tangled
• The more I learn about Fireworks the more unsavory they seem as a company. I don’t care what the license says, Moonshot has been openly improving, sharing research, and providing weights for the models that make up your entire bottom line, and the moment you can improve them in reciprocal it’s closed weights, “this is our own proprietary” nonsense? Where are we that China has better open source ethos than America?
  by nxtfari
• Ignoring for the moment issues of what "counts" as open, won't open models rapidly advance due to stuff like this in ways that it's less possible for the proprietary ones to do? This is exactly how Linux & Wikipedia, for example, overtook their "frontiers", right?
  by jamienk
• Off topic:With sol pricing drop tbh kimi k3’s value prop has not been that great. For our internal use case/testing/benchmarks sol come out with way better quality and much cheaper costs. Kimi really needs to drop their pricing (I heard it’s set by them across all the neoclouds) Sol is at 2/10 vs kimi’s 3/15
  by netvarun
• It’s the first time I know fireworks has a team doing model research. I do have a complex mood in that. On one hand, I’m always happy to see improvement of OSS models, whether that’s on intelligence or cost-efficiency. On the other hand, I would be a little worried about using fireworks as my API provider. Till the moment I saw this news, I had been using fireworks as my provider of deepseek v4 flash, because I thought fireworks acting as a role deploying OSS models and selling calculation resources, should be safe to use without worry of data being used for training since there’s no “conflict of interests”. But I would think twice now.
  by tukHelix
• This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
  by GodelNumbering