Update README.md

README.md

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----
-license: apache-2.0
----
+---
+base_model: Qwen/Qwen2.5-Coder-14B-Instruct
+abliterated_from: huihui-ai/Qwen2.5-Coder-14B-Instruct-abliterated
+quantization: nf4
+license: apache-2.0
+tags:
+  - code
+  - programming
+  - uncensored
+  - 4bit
+  - nf4
+  - qwen
+  - abliteration
+  - instruct
+library: transformers
+---
+
+# Qwen2.5-Coder-14B-Instruct-Abliterated-NF4
+
+**A 4-bit (NF4) quantized, abliteration-uncensored version of Qwen2.5-Coder-14B-Instruct**
+
+This model is a **pre-quantized 4-bit NormalFloat4 (NF4)** version of the uncensored [huihui-ai/Qwen2.5-Coder-14B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-Coder-14B-Instruct-abliterated), optimized for **low VRAM** and **fast local inference**.
+
+Ideal for **local deployment**, **edge devices**, or **low-VRAM environments** while maintaining strong coding and reasoning capabilities.
+
+---
+
+## 🚀 Features
+
+- **Base Model**: `Qwen/Qwen2.5-Coder-14B-Instruct`
+- **Abliteration**: [huihui-ai](https://huggingface.co/huihui-ai) (uncensored)
+- **Quantization**: **NF4 (4-bit)** – **pre-quantized**
+- **Efficient**: ~8–9 GB VRAM required for inference
+- **Safetensors**: Secure, modern format
+- **Framework**: Compatible with `transformers`, `vLLM`, `Oobabooga`, etc.
+
+---
+
+## 📥 Installation & Usage
+
+### 1. Install Dependencies
+
+```bash
+pip install transformers torch accelerate
+---
+
+---
+### Load the Model
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "ikarius/Qwen2.5-Coder-14B-Instruct-Abliterated-NF4"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,  # eller torch.float16
+    trust_remote_code=True
+)
+
+---
+---
+
+# Eksempel
+prompt = "Write a Python function to calculate Fibonacci numbers using memoization."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    do_sample=True,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+---
+---
+### Using with text-generation-webui (Oobabooga)
+
+python server.py \
+  --model ikarius/Qwen2.5-Coder-14B-Instruct-Abliterated-NF4 \
+  --bf16 \
+  --trust-remote-code
+
+---
+
+🤝 Credits
+
+Original Model: Qwen Team
+Abliteration: huihui-ai
+Quantization: ikarius (NF4 via auto-gptq / transformers)
+Hosting: Hugging Face Hub
+
+---
+📄 License
+Apache 2.0 (same as base model)
+
+🙌 Support
+⭐ Star this repo if you find it useful!
+🐛 Report issues on the Discussions tab.