Apollo-1-8B/README.md

base_model, tags, license, license_name, license_link, language

base_model	tags	license	license_name	license_link	language
Qwen/Qwen3-8B	text-generation-inference transformers unsloth qwen3	other	anvdl-1.0	https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md	en fr pt de ro sv da bg ru cs el uk es nl sk hr pl lt nb nn fa sl gu lv it oc ne mr be sr lb vec as cy szl ast hne awa mai bho sd ga fo hi pa bn or tg yi lmo lij scn fur sc gl ca is sq li prs af mk si ur mag bs hy zh yue my ar he mt id ms tl ceb jv su min ban pag ilo war ta te kn ml tr az uz kk ba tt th lo fi et hu vi km ja ko ka eu ht pap kea tpi sw

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

• Base model: Qwen3-8B

• Architecture: Decoder-only transformer

• Parameters: ~8B

• Context length: up to 32k tokens (inherits Qwen3 long-context support)

• Domain: General-purpose reasoning, instruction following, and code generation

• Primary applications:

  • Advanced conversational AI
  • Multi-step reasoning and problem solving
  • Knowledge assistants and tutoring systems
  • Software development and code generation

• 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=0.4–0.8
• top_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

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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

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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.