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)
Related
- GGUF quants: MindBot-Qwen3.8-27B-Synergetic-GGUF
- Full writeup: see the pinned discussion on this repo
- Pipeline repo: github.com/TheMindExpansionNetwork/mindbot-qwen38-train
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