Update README.md

README.md

@@ -18,13 +18,15 @@ I experimented with a few recipes to remove refusals while preserving most of th
 
 Note that this is fairly experimental, so it might not turn out as well as expected.
 
-## 🧠 Abliteration v2
+I recommend using these generation parameters: `temperature=1.0`, `top_k=64`, `top_p=0.95`.
+
+## ✂️ Layerwise abliteration
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/HnBRigUfoQaCnpz96jnun.png)
 
 In the original technique, a refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.
 
-Here, the model was abliterated by computing a refusal direction for each layer, independently.
+Here, the model was abliterated by computing a refusal direction based on hidden states (inspired by [Sumandora's repo](https://github.com/Sumandora/remove-refusals-with-transformers/)) for each layer, independently.
 This is combined with a refusal weight of 1.5 to upscale the importance of this refusal direction in each layer.
 
 This created a very high acceptance rate (>90%) and still produced coherent outputs.