I’ll take an open-model release over a closed SOTA any day, who’s with me?
Posted by be566@reddit | LocalLLaMA | View on Reddit | 23 comments
Posted by be566@reddit | LocalLLaMA | View on Reddit | 23 comments
VolggaWax@reddit
Can this run on 32GB VRAM?
LegacyRemaster@reddit
I ran a test on the Q5_XL version (unsloth.) I asked it to remodel a trainer (pytorch) with over 3,000 lines of code from gpt2 to a more modern architecture.
The task was completed (from gpt2 di "mistral"); the only problem was that Qwen "simplified" the code by deleting many command line arguments and then apologized.
I tested temperature 0.6 with the suggested parameters. I think with a few tweaks (prompt, settings) it might not lose context (minimax performed the same task perfectly).
What impressed me was that I had asked the same question on gpt4 reasoning high, and the improvements suggested by qwen 3.6 are objectively better in context with the trainer's goal (a small, dense, 200M-parameter model trained on an RTX 6000).
SnooPaintings8639@reddit
Similar story here, i.e. qwen 3.6 failed at simole coding task today (looped until reach max context), and.then switched to minimax to clean and fix the work.
ILikeBubblyWater@reddit
That means you dont do any serious work, so thats fine
Elegant_Tech@reddit
Qwen3.6 svg capability is mind-blowing.
PwanaZana@reddit
strong closed models are useful for those who want max power, and they're useful targets to aim for with smaller open models
a_beautiful_rhind@reddit
I'll take neither. Still using mistral and gemma.
CCloak@reddit
Opus 4.7 strongly suggest to be similar to pre-nerf 4.6, but costier. So in other words, it is not good.
Tight-Requirement-15@reddit
Impressive_Chain6039@reddit
I ran a test on the Q5_XL version (unsloth.) I asked it to remodel a trainer (pytorch) with over 3,000 lines of code from gpt2 to a more modern architecture.
The task was completed (from gpt2 di "mistral"); the only problem was that Qwen "simplified" the code by deleting many command line arguments and then apologized.
I tested temperature 0.6 with the suggested parameters. I think with a few tweaks (prompt, settings) it might not lose context (minimax performed the same task perfectly).
What impressed me was that I had asked the same question on gpt4 reasoning high, and the improvements suggested by qwen 3.6 are objectively better in context with the trainer's goal (a small, dense, 200M-parameter model trained on an RTX 6000).
AppealThink1733@reddit
More open source and less use of private companies.
LoveMind_AI@reddit
Not to mention that Opus 4.7 is absolutely awful.
TheFrenchSavage@reddit
No, this is stupid.
SporksInjected@reddit
This is kinda stupid especially when qwen models are somewhat distilled from bigger frontier models. Frontier moving forward helps everyone.
ThisWillPass@reddit
Depends on your use case.
carnyzzle@reddit
Opus costs way too much for me to even think about using lol
miniocz@reddit
I learned that 3.6 is out from this post...
Cold_Tree190@reddit
🤣
elongated_argonian@reddit
Especially with Opus' quality degrading rapidly in the last few months due to increasing demand, I'd much rather prefer a less capable model that I can guarantee will stay as reliable as the day I first used it. This requirement can only be satisfied by local, preferably open-released, models.
AurumDaemonHD@reddit
I doubt it was due to increased demand. The company has so much invested in them they r making deliberate choices to fuck their users. ID just seals the deal.
Great lesson for everyone involved.
elongated_argonian@reddit
Eh, it could be a mixture of both. I've also heard that Anthropic is planning to go public, though I may be mistaken.
Sarashana@reddit
OSS models might not be the absolute best, but they are serviceable. I will always use open and free solutions over corporate products designed to nickle and dime me and lock me into their "ecosystem".
Foreign-Beginning-49@reddit
It's just keeps getting better every week. The breakdown of space and time is the last barrier. 👌