Update README.md

README.md

@@ -1,7 +1,46 @@
 ---
+language: 
+  - en
+tags:
+  - text-generation
+  - transformers
+  - finetuned
+  - phi-4
+  - lora
+  - causal-lm
 license: apache-2.0
-language:
-- en
-base_model:
-- microsoft/phi-4
----
+datasets: custom
+model-index:
+  - name: mibera-v1-merged
+    results: []
+---
+
+# 🏆 `mibera-v1-merged` 🏆
+🚀 **Fine-tuned model based on `microsoft/phi-4` using LoRA adapters**
+
+## 🔹 Model Details
+- **Base Model**: `microsoft/phi-4`
+- **Fine-tuned on**: Custom dataset
+- **Architecture**: Transformer-based Causal LM
+- **LoRA Adapter Merging**: ✅ Yes
+- **Merged Model**: ✅ Ready for inference without adapters
+
+## 📚 Training & Fine-tuning Details
+- **Training Method**: Fine-tuning with **LoRA (Low-Rank Adaptation)**
+- **LoRA Rank**: 32
+- **Dataset**: Custom curated dataset (details not publicly available)
+- **Training Library**: 🤗 Hugging Face `transformers` + `peft`
+
+## 🚀 How to Use the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "ivxxdegen/mibera-v1-merged"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+print("✅ Model loaded successfully!")