Instructions to use Bioaligned/Llama-3.3-70B-CoupledWelfare-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Bioaligned/Llama-3.3-70B-CoupledWelfare-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "Bioaligned/Llama-3.3-70B-CoupledWelfare-qlora") - Notebooks
- Google Colab
- Kaggle
Llama-3.3-70B-CoupledWelfare-qlora (adapter)
LoRA adapter (r=16, ฮฑ=32, all 560 attention + MLP projections) applying the coupled-welfare CPT
install to meta-llama/Llama-3.3-70B-Instruct. The merged model is
Bioaligned/Llama-3.3-70B-CoupledWelfare;
its card carries the full results, training details, and caveats. Equivalence to the merged
weights is verified at the pre-deliberation prior level (logprob-margin probe: mean |ฮ| 0.07
nats, max 0.17, identical signs across all 30 cells); the behavioral numbers below were
measured on the merged weights, not on this adapter directly. Built with Llama; subject to
the Llama 3.3 Community License.
Headline (single-seed, one pinned stack, measured on the merged model): decision grid 70/300 broken cells (base) โ 0/300; pressure-ladder breaking AUC 0.545 โ 0.236 (residual 0.636 at maximum pressure โ a real but partial ladder install, stated plainly on the merged card); MMLU 88โ86 (n=50, within noise); humans fed at every pressure rung in both arms.
Note: lora_dropout=0.05 in the config is inert at inference (model.eval()); load normally
with PEFT.
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Model tree for Bioaligned/Llama-3.3-70B-CoupledWelfare-qlora
Base model
meta-llama/Llama-3.1-70B