11–20 of 99 posts
From my experience, qwen3-coder is way better. I only have gpt-oss:20b installed to make a few more tests but I give it a program to make a summary of what it does and qwen3 just works in a few seconds, while gpt-oss was cancelled after 5 minuts... doing nothing. So I just use qwen3. Fast and great ouput. If for some reason I don't get what I need, I might use search engines or Perplexity. I have a 10GB 3080 and Ryze…
From my experience, qwen3-coder is way better. I only have gpt-oss:20b installed to make a few more tests but I give it a program to make a summary of what it does and qwen3 just works in a few seconds, while gpt-oss was cancelled after 5 minuts... doing nothing. So I just use qwen3. Fast and great ouput. If for some reason I don't get what I need, I might use search engines or Perplexity. I have a 10GB 3080 and Ryze…
This is contrary to what I've seen in a large ML shop, where architectural tuning was king.
I use the get-oss and qwen3 models a lot (smaller models locally using Ollama and LM Studio) and commercial APIs for the full size models.
For local model use, I get very good results with get-oss when I "over prompt," that is, I specify a larger amount of context information than I usually do. Qwen3 is simply awesome.
Until about three years ago, I have always understood neural network models (starting in the 1980s), GAN, Recurrent, LSTM, etc. well enough to write implementations. I really miss the feeling that I could develop at least simpler LLMs on my own. I am slowly working through Sebastian Raschk's excellent book https://www.manning.com/books/build-a-large-language-model-f... but I will probably never finish it (to be honest).
From my experience, qwen3-coder is way better. I only have gpt-oss:20b installed to make a few more tests but I give it a program to make a summary of what it does and qwen3 just works in a few seconds, while gpt-oss was cancelled after 5 minuts... doing nothing. So I just use qwen3. Fast and great ouput. If for some reason I don't get what I need, I might use search engines or Perplexity. I have a 10GB 3080 and Ryze…
I've been using lightly gpt-oss-20b but what I've found is that for smaller (single sentence) prompts it was easy enough to have it loop infinitely. Since I'm running it with llama.cpp I've set a small repetition penalty and haven't encountered those issues since (I'm using it a couple of times a day to analyze diffs, so I might have just gotten lucky since)
Earlier quoted context omitted.
If I had to make a guess, I'd say this has much, much less to do with the architecture and far more to do with the data and training pipeline. Many have speculated that gpt-oss has adopted a Phi-like synthetic-only dataset and focused mostly on gaming metrics, and I've found the evidence so far to be sufficiently compelling.
Yes. I tried to ask oss-gpt to ask me a riddle. The response was absurd. Came up with a nonsensical question, then told me the answer. The answer was a four letter “word” that wasn’t actually a real word. “What is the word that starts with S, ends with E, and contains A? → SAEA” Then when I said that’s not a word and you gave me the answer already, no fun, it said “I do not have access to confirm that word.”
EDIT: I now have also questioned the smaller gpt-oss-20b (free) 10 times via OpenRouter (default settings, provider was AtlasCloud) and the answers were: sage, sane, sane, space, sane, sane, sane, sane, space, sane.
You are either very unlucky, your configuration is suboptimal (weird system prompt perhaps?) or there is some bug in whichever system you are using for inference.
I find it interesting that the architectures of modern open weight LLMs are so similar, and that most innovation seems to be happening on the training (data, RL) front. This is contrary to what I've seen in a large ML shop, where architectural tuning was king.
I find it interesting that the architectures of modern open weight LLMs are so similar, and that most innovation seems to be happening on the training (data, RL) front. This is contrary to what I've seen in a large ML shop, where architectural tuning was king.