What is the state of using quants? For chat models, a few errors or lost intelligence may matter a little. But what is happening to tool calling in coding agents? Does it fail catastrophically after a few steps in the agent?
I am interesting if I can run it on a 24GB RTX 4090.
I like the byteshape quantizations - they are dynamic variable quantization weights that are tuned for quality vs overall size. They seem to make less errors at lower "average" quantizations than the unsloth 4 bit quants. I think this is similar to variable bitrate video compression where you can keep higher bits where it helps overall model accuracy.
Should be able to run this in 22GB vram so your 4090 (and a 3090) would be safe. This model also uses MLA so you can run pretty large context windows without eating up a ton of extra vram.
edit: 19GB vram for a Q4_K_M - MLX4 is around 21GB so you should be clear to run a lower quant version on the 4090. Full BF16 is close to 60GB so probably not viable.
It's been mentioned that this model is MLA capable, but it seems like the default vLLM params don't use MLA. Seeing ~0.91MB KV Footprint per token right now. Are you getting MLA to work?
It's in the ollama library at q4_K_M, which doesn't quite fit on my 4090 with the default context length. But it only offloads 8 layers to the CPU for me. I'm getting usable enough token rates. That's probably the easiest way to get it. Not tried it with vllm but if it proves good enough to stick with then I might give it a try.
Oh, and on agents: I did give it a go in opencode last night and it seemed to get a bit stuck but I think I probably pushed it too far. I asked it to explain TinyRecursiveModels and pointed it at the git repo URL. It got very confused by the returned HTML and went into a loop. But actually getting to the point of getting content back from a tool call? Absolutely fine.
I'm thinking of giving it a go with aider, but using something like gemma3:27b as the architect. I don't think you can have different models for different skills in opencode, but with smaller local models I suspect it's unavoidable for now.
I run evals and the Todo tool doesn't help most of the time. Usually models on high thinking would maintain Todo/state in their thinking tokens. What Todo helps is for cases like Anthropic models to run more parallel tool calls. If there is a Todo list call, then some of the actions after are more efficient.
What you need to do is to match the distribution of how the models were RL-ed. So you are right to say that "do X in 200 lines" is a very small part of the job to be done.
We're finding investigating to be same-but-different to coding. Probably the most close to ours that has a bigger evals community is AI SRE tasks.
Agreed wrt all these things being contextual. The LLM needs to decide whether to trigger tools like self-planning and todo lists, and as the talk gives examples of, which kind of strategies to use with them.
Was guessing that, coding tasks are a valuable but myopic lense :)
I'm guessing a self-updating plan there is sufficient. I'm not actually convinced today's current plan <> todolist flow makes sense - in the linked PLAN.md, it gets unified, and that's how we do ai coding. I don't have evals on this, but from a year of vibes coding/engineering, that's what we experientially reached across frontier coding models & tools. Nowadays we're mixing in evals too, but that's a more complicated story.
I work a lot on testing also SWE bench verified. This benchmark in my opinion now is good to catch if you got some regression on the agent side.
However, going above 75%, it is likely about the same. The remaining instances are likely underspecified despite the effort of the authors that made the benchmark "verified". From what I have seen, these are often cases where the problem statement says implement X for Y, but the agent has to simply guess whether to implement the same for other case Y' - which leads to losing or winning an instance.
Anybody knows if one can find an inference provider that offers input token caching? It should be almost required for agentic use - first speed, but also almost all conversations start where the previous ended, so cost may end up quite higher with no caching.
I would have expected good providers like Together, Fireworks, etc support it, but I can't find it, except if I run vllm myself on self-hosted instances.
I think that people are just too quick to assume this is amazing, before it is there. Which doesn't mean it won't get there.
Somehow if I take the best models and agents, most hard coding benchmarks are at below 50% and even swe bench verified is like at 75 maybe 80%. Not 95. Assuming agents just solve most problems is incorrect, despite it being really good at first prototypes.
Also in my experience agents are great to a point and then fall off a cliff. Not gradually. Just the type of errors you get past one point is so diverse, one cannot even explain it.
I noticed a similar trends in selling on X. Put a claim, peg on some product A with good sales - Cursor, Claude, Gemini, etc. Then say, the best way to use A is with our best product, guide, being MCP or something else.
For some of these I see something like 15k followers on X, but then no LinkedIn page for example. Website is always a company you cannot contact and they do everything.
Yes. The article is click bait. With such a title I would have expected majority of the area to be dummy, but it is just structurally more silicon, exactly like a picture may be majority of its mass wood.
Your statement is incorrect. The analysis was made by a professional firm - dummy silicon shims are used because the dies are thinned, as per AMD's own disclosures. Those silicon shims are bonded to the compute and SRAM dies.
I ended up disabling copilot. The reason is that the completions do not always integrate with the rest of the code, in particular with non-matching brackets. Often it just repeats some other part of the code. I had much fewer cases of this with Cody. But, arguably, the difference is not huge. But then add on top of this choice of models.
I noticed I had a lot fewer of these problems these last few weeks. I suspect the Copilot team has put a lot more effort into quality-of-life recently.
For instance, I'd often get a problem where I'd type "foo(", and VsCode would auto-close the parenthesis, so my cursor would be in "foo(|)", but Copilot wouldn't be aware of the auto-close, so it would suggest "bar)" as a completion, leading to "foo(bar))" if I accepted it. But I haven't had this problem in recent versions. Other similar papercuts I'd noticed have been fixed.
I haven't used Cody, though, so I don't know how they compare.
It seems recent years give us a lot of licenses (for core infra software) and now for LLMs. They all say in very legalese basically: these top 5-10 tech companies will not compete fairly with us, thus they are banned from using the software. The rest are welcome to use everything.
I wonder if US monopoly regulation actually starts to work well, which I see some signs of happening, will all this license revert back to fully open source?
I am interesting if I can run it on a 24GB RTX 4090.
Also, would vllm be a good option?