LLM2026_DPO_SFT19_v18 (Silent Expert v18)

This model is a specialized fine-tune of Qwen2.5-7B designed for pure data output. Through Direct Preference Optimization (DPO), it has been trained to be a "Silent Expert"—it suppresses all conversational filler, greetings, and explanations.

🌟 Key Characteristics

• Absolute Silence: Eliminates conversational preambles like "Certainly!", "Here is the data," and concluding remarks.
• Zero Formatting Noise: Trained to avoid Markdown backticks (e.g., ```json) that often cause parsing errors in automated pipelines.
• Raw Data Focus: Outputs immediate, structured content starting from the very first character.
• Hybrid Delivery: This repository contains both a 16-bit physically merged model for standalone use and a LoRA adapter (adapter_config.json) for PEFT workflows.

🚀 Usage

1. Loading as an Adapter (PEFT)

Recommended if you want to use the model within the Unsloth or PEFT framework:

from unsloth import FastLanguageModel
from peft import PeftModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
    load_in_4bit = True,
)
model = PeftModel.from_pretrained(model, "makotonlo/LLM2026_DPO_SFT19_v18")

### 🛠️ How to apply this immediately

You can either click **"Edit model card"** in your browser and paste the text above, or run this quick Python cell in your Colab to push the update automatically:

```python
from huggingface_hub import HfApi
from google.colab import userdata

api = HfApi()
repo_id = "makotonlo/LLM2026_DPO_SFT19_v18"
token = userdata.get('HF_TOKEN')

# Define the English content
readme_content = """ (Paste the Markdown above here) """

with open("README.md", "w", encoding="utf-8") as f:
    f.write(readme_content)

api.upload_file(
    path_or_fileobj="README.md",
    path_in_repo="README.md",
    repo_id=repo_id,
    token=token
)
print("✅ README updated to standard English professional format!")