Hunter 4B is a 4-billion-parameter conversational reasoning model from Lalakai Labs, built for local inference on consumer hardware. It works through problems step by step inside... traces before giving its final answer, and is tuned for reasoning, agentic coding, tool use, and cybersecurity analysis. Weights are distributed in GGUF format for llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. GGUF stores tensors only and does not use pickled code. - 4B parameters, optimized for reasoning, coding, and security tasks - Visible reasoning in traces for math, code, and multi-step problems - Q4KM quantization runs in roughly 3.5 GB of working memory at a 4K context - Apache-2.0…
Open weights
apache-2.0
Introducing Hunter 4B by Lalakai Labs. A 4B-parameter conversational reasoning model, distributed as GGUF weights for fast local inference with llama.cpp, Ollama, LM Studio, and any other GGUF-compatible runtime. GGUF is a safe, mmap-friendly format — no pickle files here. Hunter reasons out loud: it works through problems step by step inside... traces before giving you the final answer — handy for math, code, and multi-step tasks. Despite its small footprint, Hunter-4B is built to rival the performance of models in the 10–15B parameter range on reasoning, coding, and cyber tasks. Hunter 4B is a strong fine-tune of the Qwen 3.5 4B, trained on ~180,000 high-density examples. No raw web…
Open weights
mit
