Apollo-1-8B
Apollo-1-8B is a 8 billion parameter instruction-tuned model developed by Noema Research. It is based on Qwen3-8B and optimized for advanced reasoning, instruction following, and high-performance deployment.
This model represents the large-scale member of the Apollo series, balancing strong reasoning capabilities with efficiency for multi-domain applications.
Model Overview
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Base model:
Qwen3-8B -
Architecture: Decoder-only transformer
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Parameters: ~8B
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Context length: up to 32k tokens (inherits Qwen3 long-context support)
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Domain: General-purpose reasoning, instruction following, and code generation
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Primary applications:
- Advanced conversational AI
- Multi-step reasoning and problem solving
- Knowledge assistants and tutoring systems
- Software development and code generation
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License: anvdl-1.0
Key Features
- Instruction tuning for reliable multi-step reasoning and task completion
- Extended reasoning depth compared to Apollo-1-4B for complex queries
- Long-context handling, inherited from Qwen3 architecture
- Multilingual coverage, supporting diverse languages and domains
- 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-1-8B"
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 differences between supervised, unsupervised, and reinforcement learning with examples."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024, 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-1-8B demonstrates stronger reasoning and instruction-following capabilities relative to Apollo-1-4B, with internal evaluations indicating:
- Higher accuracy on complex multi-step reasoning tasks
- More robust instruction adherence
- Reduced hallucinations in factual and structured outputs
- High efficiency for large-context tasks
A full benchmark report will be provided in a future update. For upstream performance details, see the Qwen3-8B model card.
Limitations
- Reasoning scale: While improved, Apollo-1-8B cannot match ultra-large models (14B+) on extremely complex or open-ended tasks
- Knowledge breadth: Some highly specialized or niche knowledge may be limited
- Hallucinations: May generate plausible but incorrect information
- Prompt sensitivity: Outputs remain dependent on careful prompt formulation
Responsible Use
- Do not rely on Apollo-1-8B 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-1-8B and the Qwen3 base model:
@misc{noema2025apollo8b,
title={Apollo-1-8B},
author={Noema Research},
year={2025},
howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-8B}}
}
Acknowledgements
Apollo-1-8B builds upon the Qwen3 family of models. We thank the Qwen team for open-sourcing their models and enabling derivative research.