synthetic data for support bots on 7900 XTX

[orbitgarden]

I'm trying to sanity-check my setup.

I'm mostly working with Gemma in MLX on mini PC. The part I'm trying to understand is quant drift.

Would appreciate practical advice rather than theory.

[mangovault67]

I'm trying to sanity-check my setup.

A couple questions before I guess too hard. What model, what runtime, and roughly what context size?

[forgemixer]

What worked for me was pretty simple. Before rebuilding anything, I'd test one clean baseline with Llama and compare from there. Once you know the plain path works, then add the faster path back in piece by piece.

[routecraft]

I tested this tonight. On M3 Max with PyTorch, I saw basically the same behavior. The main thing that moved was latency, not quality.

[vramridge]

I don't think that's the whole story. I get why that seems right, but on M3 Max the bottleneck felt elsewhere.

[phaseport36]

Good point. The pattern around prefix caching has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.

[audioforge]

Good point. I ended up in about the same place using Ollama. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.

If you post your exact settings, people here can probably narrow it down pretty fast.

[routecraft]

Interesting, I hadn't tried that. The pattern around KV cache pressure has been pretty similar for me. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.

[orbitgarden]

I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model.

[routecraft]

Maybe, but I had the opposite result. The reported numbers can look compelling, but in ordinary usage I didn't see the same benefit. Worth testing the simpler explanation before adopting the more interesting one.