Apollo-1-4B
Apollo-1-4B is a 4 billion parameter instruction-tuned model developed by Noema Research.
It is based on Qwen3-4B and optimized for reasoning, instruction following, and lightweight deployment at scale.
This model represents the mid-size member of the Apollo series, balancing performance and efficiency for a broad range of use cases.
Model Overview
- Base model:
Qwen3-4B - Architecture: Decoder-only transformer
- Parameters: ~4B
- Context length: up to 32k tokens (inherits Qwen3 long-context support)
- Domain: General-purpose reasoning and instruction following
- Primary applications:
- Conversational AI
- Multi-step reasoning tasks
- Education and tutoring systems
- Knowledge assistants and prototyping agents
- License: anvdl-1.0
Key Features
- Instruction tuning for consistent conversational and task-oriented responses
- Improved reasoning depth compared to Apollo-1-2B, enabling stronger performance on complex queries
- Long-context handling, inherited from Qwen3 architecture
- Multilingual coverage, retaining broad knowledge across languages
- Balanced resource requirements, deployable on high-end consumer hardware and cloud GPUs
Usage
The model is available in Hugging Face Transformers format. Example:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "NoemaResearch/Apollo-1-4B"
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 helpful reasoning assistant."},
{"role":"user", "content":"Summarize the main differences between reinforcement learning and supervised learning."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=768, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Recommended settings:
temperature=0.4–0.8top_p=0.9–0.95- Lower temperatures yield more factual and concise answers
Evaluation
Apollo-1-4B demonstrates stronger reasoning capabilities relative to Apollo-1-2B, with internal evaluations indicating:
- Higher accuracy on step-by-step reasoning tasks
- More robust instruction adherence
- Reduced hallucinations in factual settings
- Effective balance between performance and efficiency
A full benchmark report will be provided in a future update. For upstream performance details, see the Qwen3-4B model card.
Limitations
- Reasoning scale: While improved, Apollo-1-4B cannot match larger models (14B+) on complex or open-ended tasks
- Knowledge breadth: Some specialized or domain-specific knowledge remains limited
- Hallucinations: May generate plausible but incorrect information
- Prompt sensitivity: Outputs remain dependent on careful prompt formulation
Responsible Use
- Do not rely on Apollo-1-4B for critical decisions without human oversight
- Verify outputs before applying in factual, legal, or safety-critical contexts
- Avoid providing personal or sensitive data in prompts
- The model should not be used to generate unsafe, harmful, or disallowed content
Model Variants
- Full precision (safetensors) — research and high-fidelity inference
- bf16 / fp16 — efficient inference on modern accelerators
- Quantized versions (int8 / int4) — deployment in resource-constrained environments
Citation
If you use this model, please cite both Apollo-1-4B and the Qwen3 base model:
@misc{noema2025apollo4b,
title={Apollo-1-4B},
author={Noema Research},
year={2025},
howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-4B}}
}
Acknowledgements
Apollo-1-4B builds upon the Qwen3 family of models. We thank the Qwen team for open-sourcing their models and enabling derivative research.