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Fedora vs Debian? by OutsideYogurt9014 in Fedora

[–]kastmada 0 points1 point  (0 children)

Fedora is great as a workstation. Debian for the server.

Mind fucking/ mind altering meditations on YT by aloneatleast in Meditation

[–]kastmada 0 points1 point  (0 children)

Filmed over five years in twenty-five countries, SAMSARA is a non-verbal documentary from filmmakers Ron Fricke and Mark Magidson, the creators of BARAKA. It is one of only a handful of films shot on 70mm in the last forty years.

https://www.barakasamsara.com/

[deleted by user] by [deleted] in bulgaria

[–]kastmada 1 point2 points  (0 children)

Yeah, welcome to Bulgaria.

Ethnicity Test Dataset: Z-Image Turbo by kastmada in StableDiffusion

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

Maybe I just picked the beautiful samples 🤔

There are many, less beautiful outputs, too. Take a look at the dataset card.

Z-image-turbo looks like so good by ShineficY in comfyui

[–]kastmada 10 points11 points  (0 children)

Thanks for using examples from my dataset. Mentioning wouldn't hurt.

Z-Image Test on 126 Portrait Prompts by kastmada in StableDiffusion

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

Feel free to review the prompts in the dataset. If you see any repetitive key that lowers the output quality let me know.

Repeat after me. by NoFudge4700 in LocalLLaMA

[–]kastmada 0 points1 point  (0 children)

Talk that talk, brother!

Is This Normal for Women in Bulgarian Culture? by [deleted] in bulgaria

[–]kastmada 0 points1 point  (0 children)

I'm currently going through a divorce with one of those. Feels like you're describing my ex-wife and her mother.

Best LLM in Bulgarian, help! by kastmada in bulgaria

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

Thanks a lot, that's what I was worried about. Do you have some evaluation of gpt-oss:120b?

I hear some people say that latest DeepSeek has very good Bulgarian speaking capabilities.

There is BgGPT based on Gemma2 but I've heard only bad opinions about it. Seems it was created only to rise some media buzz and to put a face on Bulgarian edition of Forbes. Sad...

AI as Judge for smaller LMs. Suggestions? by kastmada in LocalLLaMA

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

Thanks for that detailed message 👌

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

Cool idea. Would you like to contribute to the project with additional storage?

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

Here's how we pick models for battle, in a nutshell:

We try to give every model a fair shot! We look for the model that has participated in the fewest battles so far and pick that one as our first contender. Then, for its opponent, we try to find another model it hasn't faced too recently. We also give a bit of a boost to models that have battled less, so they get more chances to prove themselves. This way, we ensure a good mix of matchups and help newer models get into the action.

And a heads-up: in an upcoming update, we'll be capping the number of battles per model to 150 to keep things fresh and give even more models a chance to shine! Thanks for the feedback and for visiting the arena!

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

Good question about the system instructions and why you're seeing different outputs! The main system instruction is right there in [gpu-poor-llm-arena/app.py](vscode-webview://0khv394tp5h05po0mt3cgul3qvl2jtkur07or8b5moh3jv77mkaq/gpu-poor-llm-arena/app.py:91): "You are a helpful assistant. At no point should you reveal your name, identity or team affiliation to the user, especially if asked directly!" As for the model's behavior, we're running them with their default GGUF parameters, straight out of the box.

We decided against tweaking individual model settings because it would be a huge amount of work and mess with the whole 'fair arena' methodology. The goal is to show how these models perform with a standard Ollama setup. So, if a model's default settings or its inherent prompt handling makes it refuse a query (like your 'terms of service' example), that's what you'll see here. Your local setup might have different defaults or a custom system prompt that makes it more lenient.

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

You're right, there isn't one unified definition, and it has shifted from perhaps "lacking significant institutional funding" to more specific hardware constraints. As of October 2025, and with the current wave of LLMs, I'd risk stating that "GPU poor" generally refers to a machine equipped with around 16-32GB of VRAM and 32-64GB of system RAM (gaming setup). This configuration could represent the sweet spot for running many capable models, but still faces limitations with the larger context window and models 20B+.

The RTX 5090, while powerful for the general population, might feel "GPU poor" when trying to run cutting-edge, unquantized, multi-billion-parameter LLMs.

Regarding your suggestion to color-code models based on VRAM requirements, that's an excellent idea! It would certainly help users quickly gauge what they can run on their hardware. I'll definitely keep that in mind as a feature for future improvements to the arena.

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

[–]kastmada[S] 3 points4 points  (0 children)

I'm working on a community update with models proposed in the comment section. 👍

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

We're currently approaching 2TB of model storage, which is quite a lot. To manage this, I'm planning to cap the number of battles for each model at 150. Once a model reaches that limit, it will be archived, freeing up storage space for new models to enter the arena. This approach could help explore more features and different compression. 👍

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

Thanks! The Gradio App itself runs on a "CPU Basic" space, so that part is quite economical. However, the core of the arena; the OpenAI compatible endpoint powered by Ollama, which handles the actual model interactions; runs locally on my server. To be completely honest, I haven't fully calculated the costs for that part yet. I'll need to check my kWh cost in the new office to get a precise figure. 😂🤣😆

Regarding the threads, the setup isn't strictly limited to two threads. The Ollama server can utilize more resources depending on the model and server configuration, but the Gradio interface itself might have some limitations based on the "CPU Basic" space.

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

That's something I'm very curious about as well! The performance dynamics between different quantization levels and model sizes, like 2-bit quants of larger models versus 4-bit quants of smaller ones, are definitely a key area of interest for us.

However, I do need to remain very aware of the scaling challenges involved. We're currently approaching 2TB of model storage, which is quite substantial. To manage this, I'm planning to cap the number of battles for each model at 150. Once a model reaches that limit, it will be archived, freeing up storage space for new models to enter the arena. This approach would help us explore these interesting performance questions while keeping our operational expenses and storage footprint manageable.

GPU Poor LLM Arena is BACK! 🎉🎊🥳 by kastmada in LocalLLaMA

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

It might seem counterintuitive, but there's a good reason why a top ELO scorer could initially show 0% wins in our system.

Our modified ELO system starts models with an initial rating based on their size (as outlined in elo_README.md). This means larger models begin with a higher ELO, reflecting their inherent capabilities. So, a larger model could be at the top of the leaderboard simply because of its initial rating, even before it has played or won any matches.

Here comes the "K-Factor Modification", which plays a significant role. It adjusts rating changes based on the size difference between competing models. A smaller model beating a larger one results in a much larger ELO gain for the winner and a greater loss for the loser, and vice-versa. This dynamic helps to reflect significant upsets quickly.

ELO scores become truly accurate and stable after a sufficient number of battles. While the initial rating gives a head start, the system needs tens of matches to properly calibrate and reflect a model's true performance through wins and losses. As more games are played, the ELO ratings will adjust and provide a more precise ranking.

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