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!
tukHelixtoday at 8:33 PM
Itās the first time I know fireworks has a team doing model research. I do have a complex mood in that. On one hand, Iām always happy to see improvement of OSS models, whether thatās 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 thereās no āconflict of interestsā. But I would think twice now.
netvaruntoday at 6:31 PM
Off topic:With sol pricing drop tbh kimi k3ās 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 itās set by them across all the neoclouds)
Sol is at 2/10 vs kimiās 3/15
dijittoday at 9:06 PM
Given what an experience i had with Ember-2ā¦ Iām not sure Iād want to engage with its predecessor.
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?
tangledtoday at 8:19 PM
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?
Arcurutoday at 7:26 PM
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
nicotoday at 6:43 PM
> The problem: thinking models think too much
This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesnāt require that much thinking
Itās amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications
qeternitytoday at 9:12 PM
This is undoubtedly great. But most of the inference cost today for dominant use cases (agentic coding) are in the prefill, not the decode. This is one of the reasons that DeepSeek is so aggressively optimizing prefill and caching.
andsoitistoday at 6:19 PM
> The problem: thinking models think too much
Analysis paralysis stifles not just human intelligence, but other intelligences too.
dmkolobovtoday at 9:24 PM
This is cool! But also: am I wrong for thinking āPareto frontierā is some pretty silly/clever marketing jargon? Is this common phrasing for basically saying: test performance per spend on tokens is decent?
spdustintoday at 8:23 PM
Been thinking about the feasibility of training a model using synthetic thinking traces that were reduced to caveman-speak prior to being used for training. Seems like it would be fairly easy to generate plenty of suitably lobotomized synthetic traces with a pair of cheap-ish models. Or even just using good old fashioned NLP to aggressively remove stop words and reduce trace words to lemmas.
intothemildtoday at 6:24 PM
So they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
tomrodtoday at 6:20 PM
Well done, and great iteration.
The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).
nxtfaritoday at 8:40 PM
The more I learn about Fireworks the more unsavory they seem as a company. I donāt 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 itās closed weights, āthis is our own proprietaryā nonsense? Where are we that China has better open source ethos than America?
srameshctoday at 7:50 PM
> The problem: thinking models think too much
I see that with Opus 5, it started thinking like crazy in the last few days , I don't think my workflow is that complicated, still it gets into thinking mode and stays there
riquitotoday at 7:41 PM
Aside. I find the "cost per task" charts both useful and uncanny. Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars? Or a different model that too scores 90% in 1 dollar? How much will it cost me the last 10% or 5%? At the end of the day, cost to 100% is what matters and the half (90%) backed solution may require more to reach 100% (or not, who knows?)
erichoceantoday at 6:20 PM
Need this done for DeepSeek, ideally one of the Flash models.
blissofbeingtoday at 8:01 PM
Would be nice to include in fire pass.
tdhz77today at 6:24 PM
Does anybody know if this would be a good model for creative writing?
dbuxtontoday at 6:42 PM
Do they mean Opus 5.5 or Opus 5?
themgttoday at 6:43 PM
The result? Ember-1 set a new Pareto frontier for Bedside Bench across both open and closed models including GPT-5.6 Sol, GPT-6 Astra, and Claude Opus 5 on cost/task.
"Pareto": 8 hits
"Opus 5.5": zero hits
ls612today at 6:22 PM
On the smaller end, Quen 3.8, while being extraordinarily capable for a small local model, also suffers from extreme thinking. I wonder if the techniques described here generalize to other models too.
monkey_monkeytoday at 6:19 PM
I don't think the article mentions Pareto frontier enough.
Also, did I miss a memo? Suddenly every article on AI seems to be talking about the Pareto frontier - or have I just not been paying attention?
logicalleetoday at 6:40 PM
This is really interesting. I think the Fireworks Serverless Training infrastructure they used to develop it is also unique and needed. Except if someone works at one of a handful of the largest labs, it is very difficult to set up or try any sort of training pipeline. The managed training infrastructure makes it available to more people.
esafaktoday at 6:21 PM
It looks like it would be similar to GLM 5.3 Flash, had they tested it...