Update README.md

README.md

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 ---
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct
+- lora
+- sft
+- transformers
+- trl
+- german
+- english
+- coding
+- code-generation
+- aether
+- gguf
 license: apache-2.0
+language:
+- de
+- en
 ---
+
+# Aether 2.5 Coder
+
+Aether 2.5 Coder is a specialized coding and technical reasoning model built on top of the **Qwen2.5-Coder-3B-Instruct** base architecture. Fine-tuned using SFT (Supervised Fine-Tuning) with Hugging Face TRL and PEFT (LoRA) on custom datasets, Aether 2.5 Coder combines high-precision code generation, script optimization, and debugging with multilingual instruction following in German and English.
+
+> 🚀 **Looking for GGUF versions?**
+> If you want to run Aether 2.5 Coder locally via **LM Studio**, **Ollama**, or **llama.cpp**, check out the pre-quantized GGUF repository:
+> 👉 **[Maxilicious20/Aether-2.5-Coder-3B-GGUF](https://huggingface.co/Maxilicious20/Aether-2.5-Coder-3B-GGUF)**
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Maxilicious20
+- **Model type:** Causal Language Model (LoRA Adapter)
+- **Language(s) (NLP):** German, English, Programming Languages (Python, JavaScript, C++, Luau, etc.)
+- **License:** Apache-2.0
+- **Finetuned from model:** Qwen/Qwen2.5-Coder-3B-Instruct
+
+## Uses
+
+### Direct Use
+
+Aether 2.5 Coder is tailored for automated code completion, script writing, structural refactoring, debugging, and software architecture planning. It delivers top-tier 3B coding performance while maintaining low VRAM consumption for efficient execution on consumer hardware.
+
+### Quantized & GGUF Models
+
+For standalone CPU/GPU local execution without Python/Transformers dependencies, use the quantized GGUF binaries:
+
+* 📦 **GGUF Repository:** [Maxilicious20/Aether-2.5-Coder-3B-GGUF](https://huggingface.co/Maxilicious20/Aether-2.5-Coder-3B-GGUF)
+* **Available Quantizations:**
+  * `aether_coder_f16.gguf` (Uncompressed / Full Precision)
+  * `aether_coder_q8_0.gguf` (High Quality / 8-bit)
+  * `aether_coder_q4_k_m.gguf` (Recommended / Balanced Speed & VRAM)
+
+### How to Get Started with the Model
+
+#### Python (Transformers & PEFT)
+
+Use the following Python code to load Aether 2.5 Coder with `transformers` and `peft`:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model_id = "Qwen/Qwen2.5-Coder-3B-Instruct"
+adapter_id = "Maxilicious20/Aether-2.5-Coder-3B"
+
+# Load Tokenizer and Base Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load Aether 2.5 Coder LoRA Adapter
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+# Example Prompt
+messages = [
+    {"role": "system", "content": "You are Aether 2.5 Coder, an expert AI programming assistant."},
+    {"role": "user", "content": "Write a Python script to filter and parse a JSON dataset efficiently."}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))