README.md

metadata

language:
  - en
tags:
  - text-generation
  - transformers
  - finetuned
  - phi-4
  - lora
  - causal-lm
license: apache-2.0
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

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!")