I'm using the Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS model. It's a general-purpose model capable of code generation and reverse engineering. Strata also maintains a dedicated code-specialized model.
When running Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS via the DeepSeek harness, complex reasoning workloads can occasionally get stuck in an infinite reasoning loop toward the end: it repeats identical thought trajectories and actions indefinitely, requiring manual prompt swap intervention to break out. Its output token throughput fluctuates — sometimes hitting 60 tokens/sec, other times topping 100+ tokens/sec, root cause unknown.
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Originally Posted by Farooq Ali
Any updates on how this is working out in practice?
Is it good for reversing and coding? Or just as a general purpose LLM?
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