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--- |
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base_model: |
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- Qwen/Qwen3-4B |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen3 |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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--- |
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# Nous-V1 4B |
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## Overview |
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**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. |
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**Key Features:** |
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- **⚡ Efficient 4B Parameter Scale:** Balances model capability with practical deployment on modern hardware |
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- **🧠 Enhanced Contextual Understanding:** Supports an 8,192 token context window, enabling complex multi-turn conversations and document analysis |
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- **🌐 Multilingual & Multi-domain:** Trained on a diverse dataset for broad language and domain coverage |
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- **🤖 Instruction-Following & Adaptability:** Fine-tuned to respond accurately and adaptively across tasks |
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- **🚀 Optimized Inference:** Suitable for GPU environments such as NVIDIA A100, T4, and P100 for low-latency applications |
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--- |
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## Why Choose Nous-V1 4B? |
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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: |
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- Real-time conversational agents |
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- Code completion and programming assistance |
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- Content generation and summarization |
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- Multilingual natural language understanding |
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--- |
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## 🖥️ How to Run Locally |
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You can easily integrate Nous-V1 4B via the Hugging Face Transformers library or deploy it on popular serving platforms. |
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### Using Hugging Face Transformers |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="apexion-ai/Nous-V1-4B") |
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messages = [ |
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{"role": "user", "content": "Who are you?"}, |
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] |
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pipe(messages) |
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``` |
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### Deployment Options |
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- Compatible with [vLLM](https://github.com/vllm-project/vllm) for efficient serving |
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- Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) for lightweight inference |
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--- |
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## Recommended Sampling Parameters |
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```yaml |
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Temperature: 0.7 |
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Top-p: 0.9 |
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Top-k: 40 |
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Min-p: 0.0 |
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``` |
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--- |
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## FAQ |
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- **Q:** Can I fine-tune Nous-V1 4B on my custom data? |
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**A:** Yes, the model supports fine-tuning workflows via Hugging Face Trainer or custom scripts. |
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- **Q:** What hardware is recommended? |
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**A:** NVIDIA GPUs with at least 16GB VRAM (e.g., A100, 3090) are optimal for inference and fine-tuning. |
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- **Q:** Is the model safe to use for production? |
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**A:** Nous-V1 4B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content. |
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--- |
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## 📄 Citation |
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```bibtex |
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@misc{apexion2025nousv14b, |
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title={Nous-V1 4B: Efficient Large Language Model for Versatile NLP Applications}, |
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author={Apexion AI Team}, |
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year={2025}, |
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url={https://huggingface.co/apexion-ai/Nous-V1-4B} |
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} |
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``` |
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--- |
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*Nous-V1 4B — Powering practical AI applications with intelligent language understanding.* |
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