Scr1pt-AI-Uncensored launches on HuggingFace in GGUF format for local inference
shiraandfuji released Scr1pt-AI-Uncensored on HuggingFace this week in GGUF format, enabling conversational inference on consumer hardware without safety filters.
Scr1pt-AI-Uncensored, a conversational language model from shiraandfuji, is now available on HuggingFace in GGUF quantized format. The release targets practitioners running local inference servers—llama.cpp, Ollama, LM Studio—where the model can operate on consumer CPUs and GPUs without requiring a discrete graphics card.
GGUF quantization typically cuts memory footprint by 50–75 percent compared to full-precision weights, a trade-off that makes the format standard for Apple Silicon, AMD, and Intel deployments. The model card flags conversational capability, indicating tuning for multi-turn dialogue. The "uncensored" designation signals that safety filters were removed or never applied during training—a common marker for open-weight releases intended for unrestricted local use.
Availability and adoption
The HuggingFace card lists US-region hosting and endpoint compatibility but omits parameter count, context length, and base architecture. At launch, the model had zero downloads and zero likes, typical for a fresh release before community adoption begins. No benchmark results or performance metrics are currently available on the card.