metadata

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. 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

# 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 for efficient serving
Works with llama.cpp for lightweight inference

Recommended Sampling Parameters

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

@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.