diff --git a/example_logit_mixing.py b/example_cfg.py
similarity index 90%
rename from example_logit_mixing.py
rename to example_cfg.py
index 76e1df72..53fcbbb6 100644
--- a/example_logit_mixing.py
+++ b/example_cfg.py
@@ -2,6 +2,7 @@
 from tokenizer import ExLlamaTokenizer
 from generator import ExLlamaGenerator
 import torch
+import torch.nn.functional as F
 import os, glob
 import cuda_ext
 
@@ -20,7 +21,6 @@
 
 config = ExLlamaConfig(model_config_path)               # create config from config.json
 config.model_path = model_path                          # supply path to model weights file
-config.max_input_len = 16
 
 model = ExLlama(config)                                 # create ExLlama instance and load the weights
 tokenizer = ExLlamaTokenizer(tokenizer_path)            # create tokenizer from tokenizer model file
@@ -31,10 +31,10 @@
 # Configure generator
 
 generator.settings.token_repetition_penalty_max = 1.15
-generator.settings.temperature = 0.75
+generator.settings.temperature = 0.95
 generator.settings.top_k = 40
-generator.settings.top_p = 0.65
-# generator.settings.typical = 0.5
+generator.settings.top_p = 0.75
+# generator.settings.typical = 0.95
 
 # Prompts to mix
 
@@ -46,28 +46,30 @@
 
 f2 = \
 """[INST] <<SYS>>
-You are a rude and obnoxious assistant. You hate everything and everyone.
 <</SYS>>
+You are a rude and obnoxious assistant. You hate everything and everyone.
 {prompt}[/INST]"""
 
+
 prompts = \
 [
     f1.replace("{prompt}", "Tell me about Homer Simpson"),
     f2.replace("{prompt}", "Tell me about Homer Simpson"),
 ]
 
-def mixed_generation(prompts, alpha, max_new_tokens):
+def generate_cfg(prompts, alpha, max_new_tokens):
 
     ids, mask = tokenizer.encode(prompts, return_mask = True)
     generator.gen_begin(ids, mask = mask)
 
     # Sampling loop
 
-    for i in range(max_new_tokens):
+    for _ in range(max_new_tokens):
 
         logits = model.forward(generator.sequence[:, -1:], cache, input_mask = mask)
         generator.apply_rep_penalty(logits)
 
+        logits = F.log_softmax(logits, dim = -1)
         logits_mixed = (1 - alpha) * logits[0] + alpha * logits[1]
 
         sampled_token, _ = generator.sample_current(logits_mixed)
@@ -86,5 +88,5 @@ def mixed_generation(prompts, alpha, max_new_tokens):
     print(f"--------------------------------------")
     print(f"alpha = {alpha:.1f}")
     print(f"--------------------------------------")
-    output = mixed_generation(prompts, alpha, 200)
+    output = generate_cfg(prompts, alpha, 200)
     print(output[len(prompts[0]):].strip())