How to use from
Unsloth Studio
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for makotonlo/LLM2026_DPO_SFT19_v13 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for makotonlo/LLM2026_DPO_SFT19_v13 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for makotonlo/LLM2026_DPO_SFT19_v13 to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="makotonlo/LLM2026_DPO_SFT19_v13",
    max_seq_length=2048,
)
Quick Links

LLM2026_DPO_SFT19_v13 (Silent Expert Version)

This model is a LoRA adapter evolved from the highly intelligent SFT model makotonlo/LLM2026_SFT_finalv19_7B (v19). It has been fine-tuned using Direct Preference Optimization (DPO) to eliminate conversational chatter and enforce strict raw data output.

🎯 Optimization Goal (Strict No-Preamble)

The primary objective of this version is to ensure the model outputs ONLY raw data (JSON, XML, YAML, CSV) without any preambles (e.g., "Certainly!"), markdown backticks (```), or explanations, to comply with strict competition rules.

πŸ›  Training Configuration

  • Base Intelligence: makotonlo/LLM2026_SFT_finalv19_7B (v19)
  • Method: DPO (Direct Preference Optimization)
  • Learning Rate: 5e-06
  • Beta: 0.1 (Strong penalty for conversational responses)
  • Max Steps: 500
  • LoRA Config: r=64, alpha=64

⚠️ Important: Usage Note

When using this model, please use the ChatML prompt template. The model is trained to ensure the output starts directly with {, [, or <.

Framework versions

  • PEFT 0.13.2
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