Ouro 2.6B Thinking Model GGUFs Released with Q8_0 and Q4_K_M Quantization

Ouro 2.6B is now available as GGUF quantizations, making an innovative looped inference architecture accessible to local deployment practitioners. The model ships in two practical quantization variants: Q8_0 (2.7GB) for higher quality and Q4_K_M (1.6GB) for constrained systems, both compatible with the major local inference frameworks.

Ouro represents an interesting architectural departure from standard transformers, using looped inference patterns that may offer improved reasoning capabilities. With GGUF support across LM Studio, Ollama, and llama.cpp, practitioners can immediately integrate this model into their existing local deployment stacks without additional tooling.

The dual quantization strategy demonstrates best practices for model releases: providing both quality-focused and size-optimized options ensures maximum accessibility across different hardware configurations and use cases.

Source: r/LocalLLaMA · Relevance: 8/10