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---
base_model:
- Qwen/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: cc-by-nc-sa-4.0
language:
- en
---
# 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 8,192 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.*
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