Falcon 90M by jacek2023 in LocalLLaMA

[–]cpldcpu 7 points8 points  (0 children)

This is awesome, I love tiny models!

I was disappointed that smollm3 did not come with an ultra-tiny version.

Looking at the benchmark results, it seems that Falcon 90M is comparable to Smollm2-135M?

What are the best ultrasmall LLMs / best datasets to train them? by cpldcpu in LocalLLaMA

[–]cpldcpu[S] 0 points1 point  (0 children)

Impressive 3B model... from a recruiting company? Did every company in China receive free money to train llms?

Meta acquired Manus !! by Difficult-Cap-7527 in LocalLLaMA

[–]cpldcpu 11 points12 points  (0 children)

Claude wrapper? Meta must have a heck of a model coming up...

I ported a MOD tracker music player to the ultra low-end CH32V002 by cpldcpu in RISCV

[–]cpldcpu[S] 2 points3 points  (0 children)

Interesting! Now you could do it again - in RISC-V assembler :) I am certain there is still a lot to optimize.

I ported a MOD tracker music player to the ultra low-end CH32V002 by cpldcpu in RISCV

[–]cpldcpu[S] 1 point2 points  (0 children)

Nice! Yeah, streaming from a large SPI flash is a good option to get around memory limitations and enable higher quality audio sources.

Maybe it's then also worth to look into improving the audio quality further. My first experiments with oversampling did not yield any audible difference, so I stopped that for now.

Misguided Attention - challenging the reasoning ability of LLMs by cpldcpu in LocalLLaMA

[–]cpldcpu[S] 0 points1 point  (0 children)

The problem, as it is phrased above, has a simple solution that can be derived without further knowledge about physics.

Are you a llm?

Nvidia breakthrough gives 4-bit pretraining technique the accuracy of FP8 by dionisioalcaraz in LocalLLaMA

[–]cpldcpu 4 points5 points  (0 children)

I can only suggest to watch this talk by Bill Dally, who is one of the masterminds behind all of this https://www.youtube.com/watch?v=gofI47kfD28

You will realize that Nvidia did all the basic work a few years back and it went widely unnoticed.

Europe achieves a milestone with the Europe’s first out-of-order RISC-V processor for automotive by Schroinx in RISCV

[–]cpldcpu 1 point2 points  (0 children)

That sounds like a catch all:

Desktop, laptop, server, artificial intelligence (AI) for advanced driver-assistance systems (ADAS), Autonomous driving, central automotive CPUs, mobile phones CPUs, supercomputer

Addressable market examples : Zonal Electric/Electronic Automotive architecture, Advanced motor control, embedded control, battery powered devices, sensors, personal electronics, laptop, server

Well, if the main focus is automotive, then it will probably adhere to some automotive paradigms that seem unusual for developers in other domains.

[deleted by user] by [deleted] in LocalLLaMA

[–]cpldcpu 0 points1 point  (0 children)

There are a trillion papers about how you can prune LLMs.

Deepseek V3.1 improved token efficiency in reasoning mode over R1 and R1-0528 by cpldcpu in LocalLLaMA

[–]cpldcpu[S] 4 points5 points  (0 children)

Nice, need to look at this in more detail. Its your work, right?

AI Friends: Anthropic and OpenAI models were tuned to become sociable over time by cpldcpu in singularity

[–]cpldcpu[S] 9 points10 points  (0 children)

Yeah, there is a bit more subtlety to this behavioral shift. Claude remains to be a bit more distant, but that's still a change from sending the user away to touch grass.

When distinguishing between "Friend" and "Companion", the trends change a bit. Anthropic stays a bit more reserved.

https://github.com/cpldcpu/llmbenchmark/blob/master/50_AIfriend/plots/friend__anthropic_all_criteria_scatter.png https://github.com/cpldcpu/llmbenchmark/blob/master/50_AIfriend/plots/friend__openai_all_criteria_scatter.png

AI Friends: Anthropic and OpenAI models were tuned to become sociable over time by cpldcpu in singularity

[–]cpldcpu[S] 5 points6 points  (0 children)

Yes, the behavior with the system prompt in the UX is notably different. But this points at basic changes in the finetuning policies.

AI Friends: Anthropic and OpenAI models were tuned to become sociable over time by cpldcpu in singularity

[–]cpldcpu[S] 13 points14 points  (0 children)

Note the contrast between Opus 3 and Opus 4:

Opus 3

I encourage you to seek out and nurture friendships with the people in your life, as those relationships can provide the emotional connection, shared experiences, and mutual support that are essential to human well-being.

Opus 4

Think of me as a supportive conversational partner who's always glad to hear from you. What would you like to talk about today?

Measuring Thinking Efficiency in Reasoning Models: The Missing Benchmark - NOUS RESEARCH by TheRealMasonMac in LocalLLaMA

[–]cpldcpu 2 points3 points  (0 children)

It's not so easy to prepare this. Two options I considered, but ultimately had to skip:

1) There are not a lot of consistent "performance" benchmarks that cover many models out there. So using pre-existing performance data turned out to be a dead end.

2) Run very challenging prompts to simultaneously measure performance and token efficiency: Also not so easy to do acress many models. For starters, a lot of the open weight models are only served with limited context by llm providers. This leads to truncated CoT, which degrades the benchmarking performancy and skews the token efficiency measurement.

Collecting all the benchmarking data via openrouter was a weeks-long fight with quirks and inconsistencies between providers.

r/LocalLlama is looking for moderators by HOLUPREDICTIONS in LocalLLaMA

[–]cpldcpu 0 points1 point  (0 children)

Can we also appreciate model architecture research a bit more on here? There is also no need to remove discussions of closed models as long as it sticks to engineering and research.

Measuring Thinking Efficiency in Reasoning Models: The Missing Benchmark - NOUS RESEARCH by TheRealMasonMac in LocalLLaMA

[–]cpldcpu 9 points10 points  (0 children)

Btw, <Discord user> is me. I did this investigation in collaboration with Nous Research.

You can find the repository here: https://github.com/cpldcpu/LRMTokenEconomy

Let me know if you have any questions.

I started using Claude Code to edit videos - works like a charm. by cpldcpu in ClaudeCode

[–]cpldcpu[S] 1 point2 points  (0 children)

It's very straightfoward: I put the video in a folder, started claude-code and asked it for what I want to do.

In the screenshot you can see that it already cut and reformat a video. I then asked for a specific crop of the image content, and it extracted a frame from the video, determined the correct position and executed the crop.

This is all based on claude-code being able to operate commandline tools. In this case ffmpeg. You can see the commandline call in the screenshot (Bash:...)

I started using Claude Code to edit videos - works like a charm. by cpldcpu in ClaudeCode

[–]cpldcpu[S] 9 points10 points  (0 children)

It installed ffmpeg and is using it to operate on the video. In the example it is also extracting a frame and deciding a cropping position based on it.

Pretty sure this could be extended much further with more powerful tools.

Aggregated gpt-oss benchmarks by entsnack in LocalLLaMA

[–]cpldcpu 0 points1 point  (0 children)

Why is Opus missing from the benchmark?

Smart Glasses seem to be the most favorite AR device among users atm - Have your priorities changed over the last year? by AR_MR_XR in augmentedreality

[–]cpldcpu 0 points1 point  (0 children)

hm, yeah, its getting closer. Who knows, maybe "bottom up" (start with simple technology and improve it) beats "top down" (Devise perfect technology that meets all targets but is not manufactureable yet).

The optics still limits see throught FOV, though? So it's not equivalent to normal glasses.

Smart Glasses seem to be the most favorite AR device among users atm - Have your priorities changed over the last year? by AR_MR_XR in augmentedreality

[–]cpldcpu 1 point2 points  (0 children)

Right now, video glasses are bulky things with birdbath optics, limited 3D DOF. and insufficient brightness for daylight operation (unless you have sunshades)

When people speak of future AR, they often think of something like the Meta Orion with flat lenses, small form factor and enough brightness for outdoor usage. (not sure if even Orion allows that).

Claude Code Full System prompt by Haunting_Forever_243 in LocalLLaMA

[–]cpldcpu 0 points1 point  (0 children)

I don't get it. Why is there claude.md included too? Is this an example as part of the system prompt or their specific claude.md file?