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 have been trying a mix of fine-tuning and I am amazed that most people do not see this coming.<p>A tiny, smaller than 1b parameter model, fine-tuned, can kick ass for constrained work. I do not have a lot of budget, I fine-tune only on a 16GB M4 Mac Mini. But that also tells me the potential is wild. Progress has been slow since I moonlight on this.<p>I have been trying to build a set of models + agents for full-stack development, where each model does only a small piece, like take user prompt and break into backend/frontend tasks. Then a Rust+Diesel model, a Rust+Auxum model, a Solid+Router model and so on. I know this is wild but this is just theory - can 5 or 6 Qwen 3.5 0.8b models do full-stack web development? My hunch says they can, better than what most people expect. Heck, with a good harness, it might beat all the cheaper models for the specific task, like Haiku or Luna.
I think a sub-set of people see this coming, I also think it isn’t just fine tuning open weight LLM models. A few people I know who are thinking along the same lines with architectures like BERT etc.<p>That being said it’s much easier at the moment to continue to use the frontier providers for most general tasks, that is the argument I’ve heard.<p>For creating these types of fine tuned local models, on constrained hardware for inference, I do think this is the way to go for specific tasks too!
The issue I find with this is that the frontier models still outperform the small finetuned model on its specific task. So much so that the ROI on doing fine tunes is likely negative. I would love to hear some specific example where it did provide value though, if any has any. That would be helpful to start being able to find similar cases.
There are lots of distilled models on huggingface that are much smaller than, say, Opus. They are distilled from Opus or Fable and show clear improvements. I do not have the budget to fine-tune a 30b or more parameter model but from my tiny model experiments, the results are quite clear. Again, I have only a couple small tests.<p>Have you actually fine-tuned yourself? Email categorization comes to mind and there are tons of non-LLM approaches even that will give fantastic results. How did spam filters work before LLM?<p>I think LLMs just made us think that is the only way. It is not.
remember to test against benchmarks. I would love to hear about your progress.
I use this solution for your exact use case:<p>I have a single command that fires up llama.cpp on cpu only using gemma4 e2b, answers a single question from the command line and exits. This takes about 3 seconds to load from an SSD, and is smart enough to solve exactly these "remind me of the syntax" scenarios if you dont wanna switch to a browser.
I have no experience doing this, and I dont mean this to be snarky: what is the power (and thermal) usage of running your CPU only LLM?<p>When I do something "heavy", my 9800x3D will kick on the fans and start making lot of heat. This is fine when I'm intentionally doing say a transcode, but if I'm just querying syntax it could get pretty annoying. Do you "feel" it? I know that will be pretty machine dependent.
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?<p>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.<p>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.<p>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.<p>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.
You can buy a $10 subscription for a month to generate the training data, then cancel your subscription. The trained model is yours to use (and share with others) forever.
It wouldn't cost $10 for their lifetime use if they used cheap models instead. Mistral Small 3 24B is probably substantially better than their Qwen 3 0.6B trained model and would give you about 500,000–2,000,000 English to Bash translations. If they're a heavy user, they might use $0.30 in total, ever.<p>They also used Astra for the coding, which they can't get on a $10 subscription. And then there's the actual training cost.
Yea specialized models could also be resold in a shareware style, like even if it cost 50$ to produce you'd just have to sell 10 copies of it to people for 5$.<p>Which 5$ is a pretty easy sell if its useful in any way, It's pretty easy to justify a purchase if its yours forever and doesn't use much CPU so is easy to run I mean people were spending 1000$+ on mac mini setups to run local llms or run remote agents.
Good observation! It would have to be offset with O(140k) queries to the model, which is, well, unlikely.
Just like with OSS in general, being able to distribute it is what makes the effort worthwhile.<p>This particular example is maybe a niche, but 1400 people can use a few hundred queries in a reasonable amount of time.
If it's about the latency / flow disruption, spending a few hours once could easily be worth it <i>if</i> 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
Same token burn / cost though right?<p>I think OP's point remains, if you generate 140k pairs, your local model would need to run that many to offset having just used the generator (SOTA or not) model to begin with.<p>I wonder if another approach if latency is a concern is just to do a two shot pass with Jev (perhaps given small context you'd want one to match command, then one to match args of given command) would be an extremely fast, and cheap way to do it - rather than training your own.
> Same token burn / cost though right?<p>Not quite. One, because you save on the initial query being sent multiple times. Two, because the reasoning will be very similar at the beginning; "user asked me to", "let's check what's in this project already", etc. you'll get similar actual output cost, but input and reasoning will be shorter.
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 :)
I feel like we need a good index for these kinds of specialized models, especially if you plan to open them up. The downside is a new bash version means potentially new training.
I took it more to mean “do standard Unix command-line stuff” rather than purely emitting Bash, and while e.g. POSIX does receive updates, the basic stuff using the main tools should also work in the future!
Or you could just google the syntax to accomplish the same task faster and with fewer resources.
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.
You can run a small model locally with very low latency on consumer hardware not to mention the privacy benefits.
I vaguely recall a project from a while back that did something similar without LLMs.<p>I’m really pushing my recall, but I want to say it was written in Ruby and stored pre-configured commands that it just did traditional search over.<p>I vaguely recall it working okay because 99.99% of the questions people asked were the same (“tar command to gzip a directory and strip the prefix” is something I google like once a month).
Curious about how you generated the training data? Was it just asking an existing model to generate a bunch of examples?<p>I ask cause would this be a kind of model distillation?<p>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.
It would be awesome to share your training set on hugging face if it’s easy to de-personalize it. The largest I could find was only 800 rows.
Do you have a write-up or git repo for this? Would love to learn more and/or dig into the guts<p>edit: others have asked any you have replied "soon (tm)", looking forward for that day
It is a form of distillation, as long as you're working a very narrow "trivial" topics it works perfectly.
Here's a similar project for those who want to replicate: <a href="https://github.com/ThorOdinson246/whatisit-nl2sh" rel="nofollow">https://github.com/ThorOdinson246/whatisit-nl2sh</a><p>Not my project
This is a really good story. I love it. Really hope you can share your experience, either blog post or GitHub repo
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.
You can go quite far using a human language to Bash grammar based setup but at some point the input prompts are harder to translate. The OP has existing projects that work with AST quite deeply so I assume they know about that already.<p>I am building a natural language to CSV/Excel commands for a "wrangler" type desktop app. Same issues. The MVP is being built with parsers of sorts, entirely code generated. Then I want to fine-tune a tiny model at some point.<p><a href="https://github.com/brainless/baho" rel="nofollow">https://github.com/brainless/baho</a>
Brilliant.<p>Can you (or your agent) please write a tutorial or share some good links.<p>(Namely the fine tuning part.)<p>I'd like to learn how to do this as well.
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.
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)<p>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?
Did your Astra do any RL or just SFT? did it make up any benchmark to ensure the fine-tuning was a success?
Wow that's awesome
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)<p><a href="https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP4/tree/main" rel="nofollow">https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP...</a><p>side quest, are fable distillations only wrong when it's another country?
See, you should now share it, so others can benefit without everyone having to do the same re-training :)
i also need more info!
[dead]
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.
> what a time to be alive!<p>It's good to hear you're enjoying yourself, but I suggest retiring that expression. It's really beginning to grate.
It’s not so good to hear your pessimism, but I suggest retiring spreading it online. It’s really beginning to grate.
Ehh, I’m more annoyed by people starting comments with “ehh”
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.
<a href="https://trust.fireworks.ai/" rel="nofollow">https://trust.fireworks.ai/</a><p>this is our preferred open weight token vendor<p>this work <i>may</i> 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
Seems irrelevant? Of <i>course</i> we don’t use data for training.<p>…trust me bro.<p>It’s obviously easier to believe when they’re not <i>training models</i>.<p>Eh, anyway this whole thing is just an ad:<p>> Looking to take Ember-1 one step further, and optimize it for your use case? We are also launching training support for Ember-1, enabling enterprises to build customized, token-efficient models tailored to their needs with their own data. The future of open models is specialized models trained on your specific workload.<p>Probably, I guess, fancy serverless infrastructure actually makes virtually no difference to hosting really large models that people want to use, and “just” being an inference provider for open weight models turns out to have no moat.<p>So this is a bit of a pivot to “use our training infrastructure too…!” imo.<p>Pivot? Sure. Go them. Not what I signed up for though. /shrug
I think legal jurisdiction matters in discerning these things. The EU or US both have courts that, despite anyones opinion, are regarded as having robust contract enforcement. If Fireworks is domiciled in either then claiming ZDR and instead training on the data would be an enormous financial footgun. There's reasons why companies on both sides of contracts, even those with little to no US or EU activity, agree to use US or EU courts for enforcement.<p>Hong Kong I think used to be a popular option as well, before the handover from the UK.<p>Being terminally 'online' can make people cynical about everything, but have to temper things with reality a bit too.
Last I checked they still offer no training ZDR US based hosting. It is one of my three pinned providers for deepseek v4 flash along with Parasail and Deepinfra.
That also could explain why openrouter is worth that much
[flagged]
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.
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.
They're impossible to objectively track by design and intent. Intelligence is such a nebulous target that one can find any number of metrics to support any premise: that the model is smarter, <i>and</i> that the model is dumber. We use benchmarks to attempt to standardise comparisons but these are quickly ingested into the training data and then become effectively useless. You might have heard the term "bench-maxed." Meaning that a benchmark has an effective lifespan in months.<p>They've been using the Pelican test in the /r/Codex subreddit with some success. One major finding is that OpenAI has been silently degrading the model while charging Astra prices. Another finding is that even when the model has not been silently degraded, pelican quality is significantly lower. Sometimes comically so. The general consensus right now is that the new GPT-6 Sol model is an updated <i>Terra</i> model. Many intelligence metrics are roughly similar. Meaning the most recent model updates were an attempt to rebalance compute rather than improve intelligence.<p>Ultimately I've never seen users as upset about GPT-6 Sol/Luna than I have right now. Even Astra has been noticeably degraded for me and everyone else I have asked. This is compounded by the fact that Opus 5.5 is a generational improvement at an affordable price. There is currently no competition.
it's been known since the early days of GPT-4 that they alter the models whenever they feel like it.<p><a href="https://arxiv.org/pdf/2307.09009" rel="nofollow">https://arxiv.org/pdf/2307.09009</a><p>the accusations are quite a few because people notice.
In a Codex subreddit there is a bunch of stats.
I don’t understand how there isn’t a website out there tracking this stuff already.
<a href="https://modelregression.com/" rel="nofollow">https://modelregression.com/</a>
how the hell do we even track that? and before someone says...<p>—"Benchmarks!"<p>...I'll tell that they can be gamed so easily, and they are on a consistent basis.
100%, I wish for a legislation which would require the providers to give you at least a unique hash identifying the model (and infra running it, if it affects output) - such that the same hash must give the same output given the same seed. Right now it's all just vibes
I am glad I am not the only one to notice. I feel like I've gone back to Sonnet 4 levels of incompetence!<p>With Sol 6 I am back in a world where the model writes bad code because it is lazy ("You're absolutely right, I did not [do it properly] because I did not want to edit [a normal amount of files]").
Competition is good. Without K3/GLM/DS4 etc. there would be no pressure on OpenAI to drop Sol's price.
Agreed. Even on the open weight side, GLM 5.3 has roughly equivalent performance to Kimi K3 for less than half the cost.
With DeepSeek's pricing, no other value prop has been great.
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:<p>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.<p>[1] <a href="https://philippdubach.com/posts/jev-model-router-for-pi/" rel="nofollow">https://philippdubach.com/posts/jev-model-router-for-pi/</a>
> With sol pricing drop<p>6 or 5.6? Because 6 is hot garbage
I don't get it. Half the tokens, but triple the cost on Open Router? So I pay more? Great idea otherwise...
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?<p>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 <i>gratis</i> use. It requires them to: <i>"...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."</i><p>[0] <a href="https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#L26-L30" rel="nofollow">https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#...</a>
What you say is true, and Moonshot definitely has an agreement with Fireworks, but it still just feels wrong. It’s not the America I grew up in, where if you took, you gave back. I know that’s not a very coherent and practical position, but it feels true to me.
They are giving back - money to Moonshot, most likely. Why should they give "back" to <i>you</i>? Did you make Kimi K3, so that "back" makes sense?
is that really the America you grew up in...?
presumably...
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?<p>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.<p>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.
Where do you get this "not to spend $2.3 on ever $1.0 earned" from?<p>Google might not have compelling frontier offerings, their chat harness is complete garbage compared to any other lab (in large part due to a bizarrely badly designed harness where something like code execution requires the prompt to undergo some sort of classification step, no idea what they are doing).<p>But they absolutely kill in terms of usage offerings. Google lets one subscription be used by *SIX* different google accounts on a family plan.<p>Plus I currently literally get *$40/month* of Gemini API credits on developer.google.com because they gave me a $10/month grant 4 times.<p>They give you 200 cloud compute units on google collab, this literally lets you spin up an H100 for around 40 hrs or something if you want to try spinning up local models.<p>You get Jules (huge allotment btw), Image gen, Video gen, Music Gen, antigravity usage, 5 TB of cloud storage, Notebook LLM...
Okay 5tb cloud storage is their most expensive plan. But what do you actually use video or image gen for? Or music gen? Antigravity is garbage, I guess you can do some stuff with Gemini models over API.
I was paying for Google ai and then realized that between obscure limits and gimmick features I don't really need it. Canceled my subscription and didn't even notice a difference
It's an example number, which doesn't matter much but it comes from widely reported $2.30 spent on ops for every $1 of revenue in spring of 2025 for Anthropic.
I tend to agree, but I also should highlight how expensive this shit really is.<p>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.<p>if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?<p>[0]: <a href="https://www.techspot.com/news/113214-google-records-first-negative-free-cash-flow-since.html" rel="nofollow">https://www.techspot.com/news/113214-google-records-first-ne...</a>
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.<p>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.<p>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: <a href="https://www.interconnects.ai/p/the-current-balance-of-power-in-open" rel="nofollow">https://www.interconnects.ai/p/the-current-balance-of-power-...</a>
Won't the "frontier" labs figure out whatever techniques were used and apply them to their closed models?
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.
Over on /r/LocalLLaMA there's a group that's been getting popular doing the same thing for the Qwen 27B (and other) models. - <a href="https://huggingface.co/ukisai" rel="nofollow">https://huggingface.co/ukisai</a>
Does anyone have a real-world usage feedback on this model? Because the benchmarks look, I would say, too good: it scores even higher than Qwen 3.8 27B in some benchmarks - I am not even sure how is it possible. So the question is if this is benchmaxxing or is it actually good for practical usage like coding in complex projects, research, etc.
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.<p>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."<p>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?
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.<p>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.
> to what end?<p>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.<p>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.
I think they are trying to show potential customers what is possible.
Why does Cursor or Devin make their own models? If you use their models, then you can't fallback to other people's models on openrouter or anyplace else. You just stick with them.
Vertical integration.
I 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?
Agentic work costs ~input^2. For a single message, they cost the same. For long conversations, you end up paying much less.
Because you care about how many tokens are used per task. What you said is like only caring about the price of gas and not gas mileage of your car.
it's not exactly the same, the model stills "weighs" the same<p>here, it does less work, it's more like driving half as far but still paying the same total cost<p>this being said, K3 and E1 models are priced the same at $3.00 / $0.30 / $15.00<p><a href="https://fireworks.ai/models/fireworks/kimi-k3" rel="nofollow">https://fireworks.ai/models/fireworks/kimi-k3</a><p><a href="https://fireworks.ai/models/fireworks/ember-1" rel="nofollow">https://fireworks.ai/models/fireworks/ember-1</a>
From reading the blog post, it is essentially exactly the same as the car example. It delivers the same performance on tasks, but using 40% fewer tokens. This is the same as a car getting you to the same destination but wasting less energy on excess heat, wind resistance, or whatever else affects fuel economy (I am not an expert, obviously). I am paying for an LLM to complete tasks for me, not for the intermediate tokens.
I'm with you, I wrote prior comment under the assumption that they were priced differently (from GP claim as such), but they are priced the same (on Fireworks)<p>Will be taking Ember-1 for a spin on Monday and hopefully enjoy those better MPGs
Why are you saying it drives half as far when both models complete the same task but one uses half the tokens?<p>Wouldn't the op be more correct with their gas/distance comparison?<p>Because both cars get to the same end destination (complete the same task).<p>Unless your end goal is to see the token numbers go up, but I'm not sure why that would be of interest.
I mean, Gpt-6 Luna seems a lot better in every aspect:<p><a href="https://aibenchy.com/compare/fireworks-ember-1-high/openai-gpt-6-luna-high/" rel="nofollow">https://aibenchy.com/compare/fireworks-ember-1-high/openai-g...</a>
Half the tokens presumably means tasks get done twice as fast.
It’s same price for lower latency.
the pricing is the same, where are you seeing double?
> The problem: thinking models think too much<p>This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking<p>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. <a href="https://bentossell.com/jev/" rel="nofollow">https://bentossell.com/jev/</a> (not mine).
LLMs can be too creative and often too verbose. Sometimes there is a right answer and a way to get there with the understanding of language, but despite using structured outputs, the model insists on inventing variations not in the schema or coming up with something completely different. A model like Jev that can not do those things, and can give the same output every time with given the same input, and be able to measure probabilities has many use cases.
Lots of use cases!
I've personally used it for the following:<p>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.<p>2. e-commerce catalog classification
3. quick search using anything as context and query mapping to a pre-defined set.
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<p>For example, a typical/stock LLM can’t really play Doom in real time, but a Jev-like model can. Just because of latency<p>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<p>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 ;)<p>The more options you have, the harder it becomes to be satisfied with the one you picked.
I don't know, I have a weird feeling that Ember-1 is a distil/combination of Qwen + Gemini 3 Flash.<p>Looking at the hamsters drawings in this comparison, never saw any other model make such a similar version:<p><a href="https://aibenchy.com/compare/fireworks-ember-1-low/google-gemini-3-flash-preview-medium/qwen-qwen3-8-max-0902-low/" rel="nofollow">https://aibenchy.com/compare/fireworks-ember-1-low/google-ge...</a>
Given what an experience i had with Ember-2… I’m not sure I’d want to engage with its predecessor.<p><a href="https://en.wikipedia.org/wiki/Exapunks?wprov=sfti1" rel="nofollow">https://en.wikipedia.org/wiki/Exapunks?wprov=sfti1</a>
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.
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.<p>> task and environment feedback<p>> on-policy planning and learning<p>> feedback connects decisions to their consequences<p>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.<p>Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.
Aren't Cursor Composer models like this too? At some point all the extra RL you do can be considered as proprietary information added.<p>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 <a href="https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE" rel="nofollow">https://github.com/MoonshotAI/Kimi-K3/blob/main/LICENSE</a>
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.<p>The words of a license are what the license is.
Which is fine, that’s legal according to the license
Well done, and great iteration.<p>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).
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.
that's really interesting. would people be interesting in reviewing it at <a href="https://unbenchmark.com" rel="nofollow">https://unbenchmark.com</a>?
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.
I am no longer going to be impressed by new model releases unless they introduce an entirely new paradigm of interacting with them, that is going to make the benchmarks look like everything else isn't even 5% as capable.
I work at Fireworks and it's cool to see this was posted.<p>I'd be interested to hear what people found most interesting about Ember, and what kinds of follow-up research or educational material would be useful to you all?
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.
<a href="https://lifearchitect.ai/models-table/" rel="nofollow">https://lifearchitect.ai/models-table/</a>
> The problem: thinking models think too much<p>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
Still remember diving into Ember back around 2015. Always appreciated its strong conventions, made for really maintainable apps in the long run.
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.
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?<p>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.<p>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.
So are they going to release this tuned model? Or keep it for themselves?
Could be a really interesting article but they disabled reader mode so I guess I’ll never know.
Is this a useful model or just an ad for Fireworks runtime? I can’t really tell…
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?
Does anybody know if this would be a good model for creative writing?
Would be nice to include in fire pass.
Do they mean Opus 5.5 or Opus 5?
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.<p>That's just vibes, though.
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_you_swift_qwen_38_27b_now_has_100k/" rel="nofollow">https://www.reddit.com/r/LocalLLaMA/comments/1wj3s31/thank_y...</a>
I don't think the article mentions Pareto frontier enough.<p>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?
They want it to be the best at <i>something</i>. And it's obviously not the absolute smartest. So here we are.
I guess they figure "best bang for your buck" comes off a little too colloquial.
Pareto frontier on some benchmark that I am hearing of for the first time.<p>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.
when everyones fighting to be 'somewhere in the pile' they need some way to advertise they have made progress while not being the best.
<p><pre><code> code</code></pre>
I am confused over this... I get efficiency but price seems too high to me. I didn't try the model, so it migth be beyond fast or some other quality that is not obvious.<p>Anyone knows more or used this model?
<i>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.</i><p>"Pareto": 8 hits<p>"Opus 5.5": zero hits
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.
It looks like it would be similar to GLM 5.3 Flash, had they tested it...
[flagged]
[flagged]
[dead]
[dead]
[dead]