README.md

---
base_model:
- Qwen/Qwen3-1.7B
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
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  - pt
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---
![banner](https://huggingface.co/NoemaResearch/Apollo-1-4B/resolve/main/img/banner.png)
# Apollo-1-2B

[![Model](https://img.shields.io/badge/Model-Apollo--1--2B-blue)](https://huggingface.co/NoemaResearch/Apollo-1-2B)
[![Base](https://img.shields.io/badge/Base-Qwen3--1.7B-green)](https://huggingface.co/Qwen/Qwen3-1.7B)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](LICENSE)

Apollo-1-2B is a **2 billion parameter instruction-tuned model** developed by **Noema Research**.  
It is based on [Qwen3-1.7B](https://huggingface.co/Qwen/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:

```python
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=0.5–0.9`
* `top_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:

```bibtex
@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](https://huggingface.co/Qwen) series of models.
We thank the Qwen team for making their work openly available under permissive terms, which enabled this derivative research.

---