Descriptions:
Fahd Mirza walks through a complete local installation and live evaluation of MiniCPM 5 in its 1 billion parameter variant, released by OpenBMB as a competitor to Qwen’s popular sub-2B models. The video covers cloning the Hugging Face repo, wrapping the transformer code in a Gradio interface, and running the model on an NVIDIA RTX 6000 with 48 GB of VRAM — though the model itself uses just over 2 GB, making it compatible with consumer GPUs and even CPU inference.
A key feature highlighted is the model’s hybrid reasoning mode, toggled via an “enable_thinking” flag that causes the model to pause and reason through complex problems before responding. Mirza also explains the full training recipe: pre-training on staged large-scale data, supervised fine-tuning for natural conversation, and a final reinforcement learning plus on-policy distillation (OPT) stage that improves reasoning and instruction-following. The model uses a standard LLaMA causal LM architecture, ensuring compatibility with tools like Ollama, LM Studio, vLLM, and Apple’s MLX.
Live tests include open-ended conversation with constraint-following challenges and a coding task requiring a single-file HTML5 canvas animation of a moving car with layered scenery — a task the 1B model completes with partial success. Mirza frames MiniCPM 5 1B as a meaningful step forward for on-device AI, especially for developers who need a capable small model that fits comfortably within phone or edge-device memory.
📺 Source: Fahd Mirza · Published May 25, 2026
🏷️ Format: Tutorial Demo
