Mradermacher quantizes LeeChan-Uncensored-i1 to GGUF for CPU inference

LeeChan-Uncensored-i1 is now available in GGUF format on HuggingFace, quantized by mradermacher from the leechanrx base checkpoint. The quants include imatrix calibration, a technique that improves weight-reduction accuracy by measuring activation importance across representative prompts before quantization. GGUF wraps weights for llama.cpp and Ollama, letting practitioners run the model on CPU or mixed CPU-GPU setups without the memory overhead of full-precision PyTorch.

The base model carries the "uncensored" label, signaling removal or ablation of safety refusals common in instruction-tuned checkpoints. Open-weight models like this one can be prompted or fine-tuned around content policies, making them a staple in local-inference workflows where users control filtering themselves. Mradermacher's quantization pipeline has produced GGUF variants for hundreds of open models over the past year, typically publishing within days of upstream releases.

The HuggingFace card shows zero downloads and zero likes at publication time, suggesting the quants went live within hours of this report. Early adoption numbers will clarify whether LeeChan-Uncensored-i1 fills a niche or duplicates existing uncensored checkpoints in the 7B–13B range. Watch for benchmark tables and context-length specs on the model card—neither appeared in the initial commit—and for community feedback on instruction-following quality compared to other abliterated Llama or Mistral derivatives.