Nördnytt! 🤓Ember-1
- GodelNumbering - 12712 sekunder sedanThis 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!
- tukHelix - 7086 sekunder sedanIt’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.
- soerxpso - 989 sekunder sedanI don't see why I would be interested in this model, considering the price difference. They advertise that it's the same as Kimi K3 in half the tokens. But the pricing is double the pricing of K3. So why do I care if it uses fewer tokens, if I'm paying double per token?
- netvarun - 14421 sekunder sedanOff 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
- jamienk - 15001 sekunder sedanIgnoring 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?
- tangled - 7963 sekunder sedanWhat 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?
- jonplackett - 657 sekunder sedanCould be a really interesting article but they disabled reader mode so I guess I’ll never know.
- Arcuru - 11141 sekunder sedanOver 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
- dijit - 5096 sekunder sedanGiven what an experience i had with Ember-2… I’m not sure I’d want to engage with its predecessor.
- nico - 13676 sekunder sedan> The problem: thinking models think too much
This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking
It’s 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
- andsoitis - 15157 sekunder sedan> The problem: thinking models think too much
Analysis paralysis stifles not just human intelligence, but other intelligences too.
- intothemild - 14814 sekunder sedanSo they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
- qeternity - 4746 sekunder sedanThis 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.
- spdustin - 7710 sekunder sedanBeen 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.
- tomrod - 15052 sekunder sedanWell 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).
- srameshc - 9675 sekunder sedan> 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
- dmkolobov - 4061 sekunder sedanThis is cool! But also: am I wrong for thinking “Pareto frontier” is some pretty silly/clever marketing jargon? Is this common phrasing for basically saying: test performance per spend on tokens is decent?
- nxtfari - 6704 sekunder sedanThe 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?
- riquito - 10215 sekunder sedanAside. 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?)
- erichocean - 15083 sekunder sedanNeed this done for DeepSeek, ideally one of the Flash models.
- blissofbeing - 9008 sekunder sedanWould be nice to include in fire pass.
- tdhz77 - 14868 sekunder sedanDoes anybody know if this would be a good model for creative writing?
- dbuxton - 13748 sekunder sedanDo they mean Opus 5.5 or Opus 5?
- themgt - 13672 sekunder sedanThe 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
- ls612 - 14985 sekunder sedanOn 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_monkey - 15120 sekunder sedanI 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?
- logicallee - 13880 sekunder sedanThis 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.
- esafak - 15010 sekunder sedanIt looks like it would be similar to GLM 5.3 Flash, had they tested it...
- justmeeew - 13888 sekunder sedan[dead]
- huflungdung - 15148 sekunder sedan[dead]
- fr2029 - 11901 sekunder sedan[dead]