---
base_model:
- Qwen/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: cc-by-nc-sa-4.0
language:
- en
---
![Header](./Nous-V1-Banner.png)
# Nous-V1 4B

## Overview

**Nous-V1 4B** is a cutting-edge 4 billion parameter language model developed by Apexion AI, based on the architecture of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). Designed for versatility across diverse NLP tasks, Nous-V1 4B delivers strong performance in conversational AI, knowledge reasoning, code generation, and content creation.

**Key Features:**

- **⚡ Efficient 4B Parameter Scale:** Balances model capability with practical deployment on modern hardware  
- **🧠 Enhanced Contextual Understanding:** Supports an 128k token context window, enabling complex multi-turn conversations and document analysis  
- **🌐 Multilingual & Multi-domain:** Trained on a diverse dataset for broad language and domain coverage  
- **🤖 Instruction-Following & Adaptability:** Fine-tuned to respond accurately and adaptively across tasks  
- **🚀 Optimized Inference:** Suitable for GPU environments such as NVIDIA A100, T4, and P100 for low-latency applications  

---

## Why Choose Nous-V1 4B?

While larger models can offer more raw power, Nous-V1 4B strikes a practical balance — optimized for deployment efficiency without significant compromise on language understanding or generation quality. It’s ideal for applications requiring:

- Real-time conversational agents  
- Code completion and programming assistance  
- Content generation and summarization  
- Multilingual natural language understanding  

---

## 🖥️ How to Run Locally

You can easily integrate Nous-V1 4B via the Hugging Face Transformers library or deploy it on popular serving platforms.

### Using Hugging Face Transformers

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="apexion-ai/Nous-V1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
```

### Deployment Options

- Compatible with [vLLM](https://github.com/vllm-project/vllm) for efficient serving  
- Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) for lightweight inference  

---

## Recommended Sampling Parameters

```yaml
Temperature: 0.7
Top-p: 0.9
Top-k: 40
Min-p: 0.0
```

---

## FAQ

- **Q:** Can I fine-tune Nous-V1 4B on my custom data?  
  **A:** Yes, the model supports fine-tuning workflows via Hugging Face Trainer or custom scripts.

- **Q:** What hardware is recommended?  
  **A:** NVIDIA GPUs with at least 16GB VRAM (e.g., A100, 3090) are optimal for inference and fine-tuning.

- **Q:** Is the model safe to use for production?  
  **A:** Nous-V1 4B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content.


---

## 📄 Citation

```bibtex
@misc{apexion2025nousv14b,
  title={Nous-V1 4B: Efficient Large Language Model for Versatile NLP Applications},
  author={Apexion AI Team},
  year={2025},
  url={https://huggingface.co/apexion-ai/Nous-V1-4B}
}
```

---

*Nous-V1 4B — Powering practical AI applications with intelligent language understanding.*