Show HN: Apex-1-flash – 4B LLM Finetuned on an RTX 5070

Apex-1-flash is a new 4-billion-parameter AI model that was finetuned entirely on an RTX 5070 using memory-efficient tools. Built on the Qwen3:4B base, it was trained using the Unsloth library to optimize memory usage, making it possible to run the process smoothly on consumer hardware. The model uses PyTorch, Hugging Face Transformers, and cu128 as its technical stack.

To improve its reasoning capabilities, the model was trained on the Open-CoT-Reasoning-Mini dataset, which enhances its Chain-of-Thought (CoT) abilities — meaning it can solve problems step by step, similar to how a human would reason through a complex task.

This development is significant because it demonstrates that advanced AI models can be fine-tuned and run on hardware that many people already own, such as an RTX 5070. Most advanced models require expensive cloud infrastructure or specialized servers, putting them out of reach for many hobbyists, students, and developers. Apex-1-flash shows that efficient, small-scale models can still perform reasoning tasks effectively while remaining lightweight enough for local use.

If you're curious to try it out, you can access Apex-1-flash on Hugging Face. Visit the page below and follow the instructions to run it locally on your RTX 5070 or similar GPU. The setup is designed to be user-friendly, so you don't need to be a technical expert to get started.