Qwythos-9B-v2 Fixes AI Repetition Without Hurting Reasoning
Overview
Qwythos-9B-v2 is a 9-billion-parameter text generation model developed by empero-ai that fixes repetitive output problems in the base Qwythos while preserving its reasoning capabilities. Built on the Qwen3.5-9B architecture (a hybrid combining 3:1 Gated-DeltaNet linear-attention with full attention blocks), the model ships with a native multi-token-prediction head, 1,048,576-token context via YaRN rope-scaling, and intentionally unrestricted capabilities for research and technical work. The key innovation is FTPO (Final-Token Preference Optimization), a targeted fine-tuning method that eliminates the looping/repetition problem that plagued v1 (dropping from 6.7% to 0% looping under greedy decoding) without degrading knowledge or reasoning across benchmarks. It runs on the Hugging Face transformers library with bfloat16 weights in safetensors format and uses the Qwen3.5 native ChatML-style tokenizer.
Best use cases
Long-context reasoning and analysis:The model excels at problems requiring extended chain-of-thought over long documents or codebases, thanks to its 1M-token context window and preserved reasoning performance (83.8%...
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