Instructions to use Spidy-sense/mindsync-riddle-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Spidy-sense/mindsync-riddle-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Spidy-sense/mindsync-riddle-model") - Notebooks
- Google Colab
- Kaggle
MindSync Riddle Model
LoRA fine-tune of Llama 3.2-3B-Instruct for the Mind Sync Challenge cooperative browser game.
Trained on 150 examples:
- Easy / Medium / Hard riddles
- Villain taunts
- Fake chip descriptions
Prompt format
### Instruction:
Generate a hard riddle for Mind Sync Challenge.
### Response:
Training
- Base: meta-llama/Llama-3.2-3B-Instruct
- Method: QLoRA (4-bit NF4 + LoRA r=16)
- Epochs: 3 | Final loss: 1.2468
- Hardware: T4 GPU (Google Colab)
- Downloads last month
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meta-llama/Llama-3.2-3B-Instruct