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!
A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
Given that they're just using it to avoid the googling for bash command syntax, I'm not sure they'll save in the end against the 140k training examples they generated.
If it's about the latency / flow disruption, spending a few hours once could easily be worth it if the result is actually good enough to skip googling/retries.
This is so cool - I'm aware of this in a vague way. Can you write a little tutorial or give some good links. I want this to be the next new things I do :)
The models he is using to generate training data are presumably commercial models. He is distilling their bash knowledge into a much smaller model he can run locally fast and cheap.
All synthetic data. For this usecase, it was easier because all current generation LLMs, even the small models, are really good at bash commands (and SQL queries too)), so you can reasonably start batches of cheap subagents whose output is reviewed by a more capable model and merge into main training set. After 100k, I had to standing instructions to run the generation loops selectively, meaning only update samples in a given area where we see poor capability.
If it’s one of thing that you want just for English to bash shell commands, I will create AST, it is deterministic, exceptionally fast, no tokens so no need to fine tune existing model, please let me know your thoughts.
That's a really impressive result. There are all kinds of small tasks like this I use an LLM for, but theoretically if you broke all the sub-use cases into local-only models, and had something lightweight that routed to the right model, you could have faster and cheaper workflows. E.g. something trained on the linux man pages for common commands, since it's usually quicker to ask an LLM for a specific command with flags than to consult the man pages.
Seriously, I'm using a Qwen 3.8 27B on the homelab, distilled from supposed Fable traces. Regardless, the difference is notable, less thinking, better output. Distilled / heavy quant is better than the original (imv)
I'm literally working on context/harness engineering right now (a set of opencode plugins)
Aside on the aside, I welcome this new era of really personal software. Not Ai's being sycophants, rather being able to easily and quickly change, adapt, or extend software I am not familiar with.
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
There’s a safer way to do this with nearly no added friction. Give it a read only API key. Then just ask it to write the API calls into a bash script and then read it and run it yourself. The agent can still inspect the live resources and diagnose and give you more commands to run. I do agree I wouldn’t give it create / write access.
I've done this sort of thing before but with Vast. Pre-deposited some money online, then let the LLM request and manage a training run on an allocation. Worked pretty well without risking bankruptcy.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
Golden age before the age that ends humanity. Not talking about any "rogue AI", just the known statistical models of what is coming due to climate change.
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.
this work may explain why recent models like qwen-3.8-flash and MiMo-2.6-* have not made it into their offering, which has given me reason to pause my excitement for Fireworks
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
Sol pricing dropped but so did the quality few days ago. I wonder when these companies are sued for making the terms from their side to go downwards while taking the same subscription cost.
Agreed, I think the only place where it’s still interesting is ui design. Visually kimi and muse feel much nicer than frontier models to me, but maybe it’s an artifact of everything terrible being Claude Design
I was surprised by that. I run my benchmark [1] every couple of days and was sure this model will be ath the pareto frontier, if not THE pareto frontier. But no:
Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.
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?
> 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?
I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.
So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence-of-size inference frontier. I think “underdog” perception is illusory/temporary, not stupidity - calculated, conscious, longer term bet.
I tend to agree, but I also should highlight how expensive this shit really is.
in one month, Google actually went cash-negative. [0] even still, they are subsidizing their stuff a lot less, have the most opaque and variable limits, and increase adoption through bundling and shuffling features. I can't even share my Google One storage without subscribing to a Google AI plan anymore, but previously any plan except Google One Lite was shareable.
if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
"Oh darn, you know that thing I made and released with explicit, precise language defining who can use it and what, if any, restrictions apply? Well now someone is using it in complete accordance with those conditions I set out, and that's somehow making me upset"
The difference between contributing to OS and AI, is that the first is a hobby alternative to woodworking or hiking, while the other can easily bootstrap you a company you can get millions in investment, at least for time being.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
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
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?
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
The end: make lots of money.
The means: systematically take existing reasoning models, do some more post-training of some sort to make them achieve the same outputs with less reasoning tokens (ie, cheaper). Same quality but cheaper is always valuable.
It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.
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
Lots of use cases!
I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification
3. quick search using anything as context and query mapping to a pre-defined set.
LLM inference has two very different regimes of work: prefill & decode. You can think of the former roughly as processing a pre-specified prompt, and the latter as sequential processing (auto-regressive token generation) eg. "chain of thought". The latter is very important for LLMs and cannot be ignored; it deeply influences infra design, even necessitates copious amounts of high-bandwidth memory. Jev-like models can ignore the latter and therefore optimize much better for the former, consequently operating at both better cost and latency.
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.
Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses.
There is little to no point reading the article as well. It's stripped of all alpha.
> task and environment feedback
> on-policy planning and learning
> feedback connects decisions to their consequences
These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.
Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
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.
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).
This 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?
I don't see why? It is a well defined term that existed prior to the recent AI bubble/revolution, and from what I can see they are using it appropriately.
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?
Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."
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
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?)
> Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars?
It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...
The 90% and 95% are against some blend of tasks meant to be broadly representative. A pricey model seldom fails a problem that cheap models do well, so there's stratification of tasks by difficulty. Someone doing novel research may be in the "hard" 15% of the blend, where P(solution) goes from one third to two thirds.
On the other hand, if it's cheap to tell whether you got a good solution, and you think the 90 and 95% apply to your task blend, then it's almost always worth trying the cheap model first.
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.
Obviously this research was done before 6.0 Sol and Opus 5.5 came out. Your point stands that the frontier moves quickly and small gains can be eclipsed quickly.
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.
I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality.
I would really love if we brought back some colloquialisms in this field. Not that long ago most folks in tech would have had pretty blank looks on their faces when someone started talking about the "Pareto frontier"
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.
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!
I don't understand. If you have a model that can do bash examples already (your subagents), then why would you need to train a model?
Or are the subagents generating your training data using a closed/paid model?
A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
Given that they're just using it to avoid the googling for bash command syntax, I'm not sure they'll save in the end against the 140k training examples they generated.
Good observation! It would have to be offset with O(140k) queries to the model, which is, well, unlikely.
If it's about the latency / flow disruption, spending a few hours once could easily be worth it if the result is actually good enough to skip googling/retries.
you can probably generate quite a few example pairs in a single shot, you also likely don't need the best models for this either
This is so cool - I'm aware of this in a vague way. Can you write a little tutorial or give some good links. I want this to be the next new things I do :)
Better yet package it up in a skill!
The models he is using to generate training data are presumably commercial models. He is distilling their bash knowledge into a much smaller model he can run locally fast and cheap.
Curious about how you generated the training data? Was it just asking an existing model to generate a bunch of examples?
I ask cause would this be a kind of model distillation?
I have a small model I'm looking to train on some data, and I have some real live data but I'd love to be able to extend it.
All synthetic data. For this usecase, it was easier because all current generation LLMs, even the small models, are really good at bash commands (and SQL queries too)), so you can reasonably start batches of cheap subagents whose output is reviewed by a more capable model and merge into main training set. After 100k, I had to standing instructions to run the generation loops selectively, meaning only update samples in a given area where we see poor capability.
Do you have a write-up or git repo for this? Would love to learn more and/or dig into the guts
edit: others have asked any you have said "soon (tm)"
It is a form of distillation, as long as you're working a very narrow "trivial" topics it works perfectly.
Did your Astra do any RL or just SFT? did it make up any benchmark to ensure the fine-tuning was a success?
If it’s one of thing that you want just for English to bash shell commands, I will create AST, it is deterministic, exceptionally fast, no tokens so no need to fine tune existing model, please let me know your thoughts.
That's a really impressive result. There are all kinds of small tasks like this I use an LLM for, but theoretically if you broke all the sub-use cases into local-only models, and had something lightweight that routed to the right model, you could have faster and cheaper workflows. E.g. something trained on the linux man pages for common commands, since it's usually quicker to ask an LLM for a specific command with flags than to consult the man pages.
> That's a really impressive result.
we dont know what the result is and how its impressive.
> what a time to be alive!
It's good to hear you're enjoying yourself, but I suggest retiring that expression. It's really beginning to grate.
Seriously, I'm using a Qwen 3.8 27B on the homelab, distilled from supposed Fable traces. Regardless, the difference is notable, less thinking, better output. Distilled / heavy quant is better than the original (imv)
https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP...
side quest, are fable distillations only wrong when it's another country?
Sorry for the aside, but I noticed half the usecase of AI is fixing the awful DX.
I appreciate the aside. Interesting observation
I'm literally working on context/harness engineering right now (a set of opencode plugins)
Aside on the aside, I welcome this new era of really personal software. Not Ai's being sycophants, rather being able to easily and quickly change, adapt, or extend software I am not familiar with.
what hardware are you using to train?
I didn't have a local GPU, so I asked it to go out and find hardware. It found a google TPU v6e which seemed reasonably priced. I gave it my google api key. I told it to use TPU only when training and bring it down afterwards. That's about it.
> I gave it my google api key
This is the part where the narrator looks at the camera and says "Don't try this at home, kids!"
You’re absolutely right, I shouldn’t have rented a 200 GPU cluster for $35,000/hour. That’s on me.
[Search: Can I refund Google cloud?]
It looks like we’re not able to ask for a refund since we did actually use all of that compute intentionally.
Would you like me to write you a pleading email to send to the support team?
There’s a safer way to do this with nearly no added friction. Give it a read only API key. Then just ask it to write the API calls into a bash script and then read it and run it yourself. The agent can still inspect the live resources and diagnose and give you more commands to run. I do agree I wouldn’t give it create / write access.
Why? Isnt the API key scoped to a project and specifically made for this?
Are you confusing this with an OAuth token or something?
Until astra goes bonkers and use the tpu for days
this is what billing caps are for
https://docs.cloud.google.com/billing/docs/how-to/budgets-sp...
I've done this sort of thing before but with Vast. Pre-deposited some money online, then let the LLM request and manage a training run on an allocation. Worked pretty well without risking bankruptcy.
What kind of observability did you have over this process? I’m interested in how my peers are operating these efforts.
On the cloud side, nothing valuable existed, so the training couldn't ruin anything it didn't create. On the laptop side, I usually ask the agents to create named scripts for everything it needs to access, then those local script directory is green-lit with approve all. For cost, I kept giving it new budget in the 20-30 dollar increments.
I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.
Perhaps I wasn’t clear. What kind of instrumentation and alerting, if any, did you employ to keep an eye on it?
> I told it to use TPU only when training and bring it down afterwards.
I wouldn't put my house on it. Brave.
Neat!
i also need more info!
I am thinking about opensourcing everything, although this is not my main domain or my main startup, so the overhead of huggingface etc seems a bit unnecessary
Edit: will do as soon as possible
just ask the agent to write it up if you don't have time to do a write-up yourself
+1, would like to see. Even if it's not fully "ready for consumption", it's probably enough to reproduce the results.
Please do! Small, specialized models need more love and the time you spent would be a gift!
Would also love to read a write-up about this!
Golden age before the age that ends humanity. Not talking about any "rogue AI", just the known statistical models of what is coming due to climate change.
Do those statistical models account for declining birth rates or are they based on prior population growth projections?
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.
Just read the terms of service and read this blog post and I think your concern will be addressed.
https://trust.fireworks.ai/
this is our preferred open weight token vendor
this work may explain why recent models like qwen-3.8-flash and MiMo-2.6-* have not made it into their offering, which has given me reason to pause my excitement for Fireworks
That also could explain why openrouter is worth that much
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
Agreed. Even on the open weight side, GLM 5.3 has roughly equivalent performance to Kimi K3 for less than half the cost.
Competition is good. Without K3/GLM/DS4 etc. there would be no pressure on OpenAI to drop Sol's price.
With DeepSeek's pricing, no other value prop has been great.
Sol pricing dropped but so did the quality few days ago. I wonder when these companies are sued for making the terms from their side to go downwards while taking the same subscription cost.
Is anybody tracking these quality changes? All I've seen so far are accusations (quite a few at this point) but not really any actual data.
I don’t understand how there isn’t a website out there tracking this stuff already.
how the hell do we even track that? and before someone says...
—"Benchmarks!"
...I'll tell that they can be gamed so easily, and they are on a consistent basis.
In a Codex subreddit there is a bunch of stats.
Agreed, I think the only place where it’s still interesting is ui design. Visually kimi and muse feel much nicer than frontier models to me, but maybe it’s an artifact of everything terrible being Claude Design
I was surprised by that. I run my benchmark [1] every couple of days and was sure this model will be ath the pareto frontier, if not THE pareto frontier. But no:
Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.
[1] https://philippdubach.com/posts/jev-model-router-for-pi/
> With sol pricing drop
6 or 5.6? Because 6 is hot garbage
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?
> 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?
I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.
So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.
I think people make mistake here, google’s approach is not to spend $2.3 on every $1.0 earned, they’re riding on serving to masses “luna”, they absolutely have way more powerful models internally but they don’t clutter their infrastructure with fragile and costly intelligence-of-size inference frontier. I think “underdog” perception is illusory/temporary, not stupidity - calculated, conscious, longer term bet.
I tend to agree, but I also should highlight how expensive this shit really is.
in one month, Google actually went cash-negative. [0] even still, they are subsidizing their stuff a lot less, have the most opaque and variable limits, and increase adoption through bundling and shuffling features. I can't even share my Google One storage without subscribing to a Google AI plan anymore, but previously any plan except Google One Lite was shareable.
if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?
[0]: https://www.techspot.com/news/113214-google-records-first-ne...
Diff people have diff motives to experiment, then new work is done on top of stuff that "hits" in a way no one anticipated. Then work gets piled on top in a way that might make it hard to port
> then new work is done on top of stuff that "hits" in a way no one anticipated.
Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.
Greatness cannot be planned.
No, because close labs/models borrow but don't contribute back.
This was exactly the crux of Nathan Lambert's recent testimony to a group of US Congressional members/staff: https://www.interconnects.ai/p/the-current-balance-of-power-...
Won't the "frontier" labs figure out whatever techniques were used and apply them to their closed models?
Like how the last 2 decades of tech companies are thinly veiled open source pilfering into business units.
"Oh darn, you know that thing I made and released with explicit, precise language defining who can use it and what, if any, restrictions apply? Well now someone is using it in complete accordance with those conditions I set out, and that's somehow making me upset"
If they can keep up.
The lock-in is less pronounced as it is with AWS or MS.
The difference between contributing to OS and AI, is that the first is a hobby alternative to woodworking or hiking, while the other can easily bootstrap you a company you can get millions in investment, at least for time being.
Yes, absolutely, but only if people keep contributing in the open.
not necessarily, just knowing something is possible will motivate others to achieve it somehow. Which is why there are so many LLMs and OAI doesn't have a monopoly
Given what an experience i had with Ember-2… I’m not sure I’d want to engage with its predecessor.
https://en.wikipedia.org/wiki/Exapunks?wprov=sfti1
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
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?
> to what end?
I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.
Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.
The end: make lots of money. The means: systematically take existing reasoning models, do some more post-training of some sort to make them achieve the same outputs with less reasoning tokens (ie, cheaper). Same quality but cheaper is always valuable.
It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.
I think they are trying to show potential customers what is possible.
Vertical integration.
> 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
What are the useful applications of Jev so far? Not to sound dismissive, I just haven’t seen what people are using it for yet.
Here's a third-party (not Jev) showcase of things people built, which helped me kind of get the appeal. https://bentossell.com/jev/ (not mine).
Lots of use cases! I've personally used it for the following:
1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.
2. e-commerce catalog classification 3. quick search using anything as context and query mapping to a pre-defined set.
At least for 1, evils, you’d want to use a good old reasoning model to get the best eval results.
Why not using a cheap LLM with thinking completely disabled ? I don't think it will be much more expensive than jev.
I’ve tested this with some local LLMs and their accuracy is in general better than Jev/Laya, but they are super slow in comparison as well
For example, a typical/stock LLM can’t really play Doom in real time, but a Jev-like model can. Just because of latency
Of course, if you want the best Doom player, there are way better and faster adhoc models
LLM inference has two very different regimes of work: prefill & decode. You can think of the former roughly as processing a pre-specified prompt, and the latter as sequential processing (auto-regressive token generation) eg. "chain of thought". The latter is very important for LLMs and cannot be ignored; it deeply influences infra design, even necessitates copious amounts of high-bandwidth memory. Jev-like models can ignore the latter and therefore optimize much better for the former, consequently operating at both better cost and latency.
> The problem: thinking models think too much
Analysis paralysis stifles not just human intelligence, but other intelligences too.
The thinking traces on some Chinese models just output the full response in the thinking trace, then output it again to the user, which is redundant.
Yes and thar makes you wonder if the Paradox of Choice would apply as well ;)
The more options you have, the harder it becomes to be satisfied with the one you picked.
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.
So they trained a model on open weights, and then aren't releasing the weights... am I reading this right?
Technically kimi k-3 weights license is not open weight (it has a lot of restrictions). I would classify it as ‘weight open’ similar to the bsl and fsl ’source open’ licenses.
weight available
It happens. Most open licenses aren't GPL style copyleft.
There is little to no point reading the article as well. It's stripped of all alpha.
> task and environment feedback
> on-policy planning and learning
> feedback connects decisions to their consequences
These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.
Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.
This is precisely my point.
Aren't Cursor Composer models like this too? At some point all the extra RL you do can be considered as proprietary information added.
Not suggesting this is right or wrong, but is sort of the nature of the technology.
It happens with open source software all the time, why would we expect any different with open source weights.
Because we do. The GPL isn't a suggestion. If you can take open source code and make private software out of it then what are we all doing? No, license requirements and agreement are law for a reason.
Kimi K3 has its own license which is permissive, it isn't at all like GPL https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE
GPL is a specific license, it’s not FLOSS as a whole
Because the licenses that apply to software make no sense in the context of LLMs. With the latter, there is no source code to license.
The words of a license are what the license is.
Which is fine, that’s legal according to the license
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.
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).
Unfortunately, it’s hard to make a chart of that.
This 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?
I don't see why? It is a well defined term that existed prior to the recent AI bubble/revolution, and from what I can see they are using it appropriately.
Fair enough!
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?
Why is proprietary-licensed software unethical?
Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."
[0] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#...
> 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
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?)
> Is It better a model that takes me to 90% in 1 dollar or one that takes me to 95% in 2 dollars?
It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...
The 90% and 95% are against some blend of tasks meant to be broadly representative. A pricey model seldom fails a problem that cheap models do well, so there's stratification of tasks by difficulty. Someone doing novel research may be in the "hard" 15% of the blend, where P(solution) goes from one third to two thirds.
On the other hand, if it's cheap to tell whether you got a good solution, and you think the 90 and 95% apply to your task blend, then it's almost always worth trying the cheap model first.
Need this done for DeepSeek, ideally one of the Flash models.
And GLM. Both Deepseek 4.1 Flash and GLM 5.3 Flash are quote verbose when thinking.
If you have the compute, I have the expertise.
Would be nice to include in fire pass.
Does anybody know if this would be a good model for creative writing?
> model
> creative
Choose one.
Are you a bot?
You really have no sense of irony, do you?
Do they mean Opus 5.5 or Opus 5?
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
Obviously this research was done before 6.0 Sol and Opus 5.5 came out. Your point stands that the frontier moves quickly and small gains can be eclipsed quickly.
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.
I suspect it might generalize to other large models, but I don't think Qwen3.8 27B is one of them. Kimi K3 is a 2.8 trillion parameter model, and I suspect that is playing a big role in being able to reduce the length of CoT without taking a hit in quality.
That's just vibes, though.
https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_y...
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?
I guess they figure "best bang for your buck" comes off a little too colloquial.
I would really love if we brought back some colloquialisms in this field. Not that long ago most folks in tech would have had pretty blank looks on their faces when someone started talking about the "Pareto frontier"
Pareto frontier on some benchmark that I am hearing of for the first time.
Kimi K3 with less reasoning tokens isn't exactly exciting either, and particularly so if the license is less open than original Kimi K3.
Is there a Pareto frontier for the number of times articles mention or don't mention a Pareto frontier.
They want it to be the best at something. And it's obviously not the absolute smartest. So here we are.
when everyones fighting to be 'somewhere in the pile' they need some way to advertise they have made progress while not being the best.
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.
I can’t help but think it’s more expensive tinker.
It looks like it would be similar to GLM 5.3 Flash, had they tested it...