Instructions to use TheMindExpansionNetwork/mindforge-qwen36-27b-director-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TheMindExpansionNetwork/mindforge-qwen36-27b-director-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use TheMindExpansionNetwork/mindforge-qwen36-27b-director-lora with 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/mindforge-qwen36-27b-director-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/mindforge-qwen36-27b-director-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/mindforge-qwen36-27b-director-lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="TheMindExpansionNetwork/mindforge-qwen36-27b-director-lora", max_seq_length=2048, )
MindForge Qwen3.6 27B Director LoRA
Adapter trained from TheMindExpansionNetwork/M1ND3XPAND3RS-VOICE-VoxCPM-ready Director lane.
This is not a TTS model. It is a chat-SFT Director adapter intended to emit strict MindForge / vLLM-Omni JSON for routing voice, image, music, and video jobs while keeping spoken TTS transcripts clean and tag-free.
Training receipt
- Run ID:
qwen36-27b-mindforge-director-smoke-20260513T081919Z - Base model:
unsloth/Qwen3.6-27B - Dataset:
TheMindExpansionNetwork/M1ND3XPAND3RS-VOICE-VoxCPM-ready - Train file prefix:
qwen_director - Max steps: 60
- Train rows used: 256
- Eval rows used: 42
- Batch: 2
- Gradient accumulation: 2
- Effective batch: 4
- LoRA rank/alpha: 32/32
- Final train loss: 0.40606151446700095
- Runtime: 661.7805 seconds
- GPU: Modal A100-80GB
Files
adapter/adapter_model.safetensorsadapter/adapter_config.json- tokenizer/processor files
TRAINING_RESULT.json
Safety note
Control tags belong in Director prompts/metadata only. Do not leak routing/control tags into spoken TTS transcript text.
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