Apollo-1-2B
Apollo-1-2B is a 2 billion parameter instruction-tuned model developed by Noema Research.
It is based on Qwen3-1.7B and optimized for general reasoning, language understanding, and lightweight deployment.
This model is the first release in the Apollo series, intended as a foundation for scalable experimentation and real-world applications in constrained environments.
Model Overview
- Base model:
Qwen3-1.7B - Architecture: Decoder-only transformer
- Parameters: ~2B
- Context length: up to 32k tokens (inherits Qwen3 long-context support)
- Domain: General-purpose reasoning and instruction following
- Primary applications:
- Conversational AI
- Lightweight reasoning tasks
- Education and tutoring
- Prototype agents and assistants
- License: anvdl-1.0
Key Features
- Instruction tuned: More reliable responses in conversational and task-oriented settings
- Lightweight deployment: Optimized for environments with limited compute or memory resources
- Extended context: Inherits long-context capability from Qwen3 base
- Balanced outputs: Improved refusal behaviors and reduced hallucinations compared to the base model
- Multilingual ability: Retains multilingual knowledge from Qwen3 family
Usage
The model is available in Hugging Face Transformers format. Example:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "NoemaResearch/Apollo-1-2B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
messages = [
{"role":"system", "content":"You are Apollo, a reasoning assistant."},
{"role":"user", "content":"Explain the difference between supervised and unsupervised learning."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Recommended settings:
temperature=0.5–0.9top_p=0.85–0.95- For structured outputs (e.g. JSON), use lower temperatures for stability
Evaluation
Apollo-1-2B has been evaluated internally on a range of reasoning and language tasks. Key findings:
- Improved instruction following relative to Qwen3-1.7B
- More concise and accurate responses in structured tasks
- Maintains multilingual performance from the base model
- Effective for lightweight assistant applications
Future work will include publishing comprehensive benchmark comparisons against other models in the 1–3B parameter range.
Limitations
- Reasoning depth: As a 2B parameter model, Apollo cannot match larger-scale LLMs on complex reasoning tasks
- Knowledge coverage: May lack depth in specialized or low-resource domains
- Hallucinations: Although reduced, the model may still generate incorrect or fabricated information
- Sensitivity to prompts: Outputs vary with prompt phrasing; careful prompt design recommended
Responsible Use
- Do not rely on Apollo for critical decision-making without human oversight
- Generated outputs may contain inaccuracies; verification is required for factual or sensitive use cases
- Avoid providing personal, private, or sensitive information in prompts
- This model should not be used to generate disallowed, unsafe, or harmful content
Model Variants
- Full precision (safetensors) — research and full-fidelity inference
- bf16 / fp16 — optimized for inference on GPUs/TPUs
- Quantized versions (int8 / int4) — for deployment in constrained hardware environments
Citation
If you use this model, please cite both Apollo-1-2B and the Qwen3 base model:
@misc{noema2025apollo,
title={Apollo-1-2B},
author={Noema Research},
year={2025},
howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-2B}}
}
Acknowledgements
Apollo-1-2B builds upon the Qwen3 series of models. We thank the Qwen team for making their work openly available under permissive terms, which enabled this derivative research.