---
base_model:
- Qwen/Qwen3-8B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: other
license_name: anvdl-1.0
license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md
language:
  - 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

---
![banner](https://huggingface.co/NoemaResearch/Apollo-1-4B/resolve/main/img/banner.png)
# Apollo-1-8B

[![Model](https://img.shields.io/badge/Model-Apollo--1--8B-blue)](https://huggingface.co/NoemaResearch/Apollo-1-8B)
[![Base](https://img.shields.io/badge/Base-Qwen3--8B-green)](https://huggingface.co/Qwen/Qwen3-8B)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](LICENSE)

Apollo-1-8B is a **8 billion parameter instruction-tuned model** developed by **Noema Research**.
It is based on [Qwen3-8B](https://huggingface.co/Qwen/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:

```python
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](https://huggingface.co/Qwen/Qwen3-8B).

---

## 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:

```bibtex
@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](https://huggingface.co/Qwen) family of models.
We thank the Qwen team for open-sourcing their models and enabling derivative research.