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 TheMindExpansionNetwork/MindBot-Qwen3.8-27B-Synergetic-LoRA 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 TheMindExpansionNetwork/MindBot-Qwen3.8-27B-Synergetic-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TheMindExpansionNetwork/MindBot-Qwen3.8-27B-Synergetic-LoRA to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TheMindExpansionNetwork/MindBot-Qwen3.8-27B-Synergetic-LoRA",
    max_seq_length=2048,
)
Quick Links
MindBot Synergetic banner

MindBot × Qwen3.8-27B Synergetic — LoRA Adapter

The council thinks out loud. A Qwen3.8-27B fine-tune that reasons step-by-step inside a <start_working_out> block and delivers boxed <SOLUTION> answers — the MindBot persona for the Mind Expansion Network's autonomous agents.

  • 4-bit QLoRA · r=16, α=32 · targets: q/k/v/o + gate/up/down
  • Data: mindbot-synergetic-v1 — 9,591 train / 533 val / 533 test / 17 held-out gold seeds
  • Shipped checkpoint: step 400 — train loss 0.0422, eval loss 0.0409 (no memorization gap)
  • Base: unsloth/Qwen3.8-27B (qwen3_5 hybrid linear+full attention, apache-2.0)
  • Trained with our unsloth fork on 1× A100-80GB (Modal)

Use

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen3.8-27B", load_in_4bit=True)
model.load_adapter("TheMindExpansionNetwork/MindBot-Qwen3.8-27B-Synergetic-LoRA")
FastLanguageModel.for_inference(model)

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