Apollo-1-2B: A Lightweight, Instruction-Tuned LLM

Apollo-1-2B, developed by Noema Research, is a 2 billion parameter instruction-tuned language model built upon the Qwen3-1.7B base. It represents the inaugural release in the Apollo series, focusing on providing a foundation for scalable AI experimentation and deployment in resource-limited settings.

Key Capabilities & Features

Instruction-Tuned: Delivers more reliable responses in conversational and task-oriented scenarios.
Lightweight Deployment: Specifically optimized for environments with constrained computational and memory resources.
Extended Context: Inherits the long-context capability from the Qwen3 base, supporting up to 32k tokens.
Balanced Outputs: Features improved refusal behaviors and a reduction in hallucinations compared to its base model.
Multilingual Ability: Retains the multilingual knowledge inherent to the Qwen3 family.
General-Purpose Reasoning: Optimized for general reasoning and language understanding tasks.

Primary Applications

Apollo-1-2B is well-suited for a variety of applications where efficiency and instruction following are crucial:

Conversational AI
Lightweight Reasoning Tasks
Education and Tutoring
Prototype Agents and Assistants

Limitations

As a 2 billion parameter model, Apollo-1-2B has inherent limitations, including less reasoning depth than larger LLMs, potential knowledge gaps in specialized domains, and a susceptibility to prompt phrasing. While reduced, hallucinations may still occur, necessitating human oversight for critical applications.