Update README.md

README.md

@@ -1,21 +1,103 @@
 ---
-base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
+base_model:
+- Qwen/Qwen3-1.7B
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - qwen3
-license: apache-2.0
+license: cc-by-nc-sa-4.0
 language:
 - en
 ---
+![Header](./Nous-V1-Banner.png)
+# Nous-V1 8B
 
-# Uploaded finetuned  model
+## Overview
 
-- **Developed by:** apexion-ai
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen3-1.7b-unsloth-bnb-4bit
+**Nous-V1 2B** is a cutting-edge 8 billion parameter language model developed by Apexion AI, based on the architecture of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). Designed for versatility across diverse NLP tasks, Nous-V1 4B delivers strong performance in conversational AI, knowledge reasoning, code generation, and content creation.
 
-This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+**Key Features:**
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+- **⚡ Efficient 2B 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 2B?
+
+While larger models can offer more raw power, Nous-V1 2B 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 2B 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-V1-2B")
+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 2B 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 2B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content.
+
+
+---
+
+## 📄 Citation
+
+```bibtex
+@misc{apexion2025nousv14b,
+  title={Nous-V1 2B: Efficient Large Language Model for Versatile NLP Applications},
+  author={Apexion AI Team},
+  year={2025},
+  url={https://huggingface.co/apexion-ai/Nous-V1-2B}
+}
+```
+
+---
+
+*Nous-V1 2B — Powering practical AI applications with intelligent language understanding.*