Update README.md

README.md

@@ -42,38 +42,3 @@ As is, CARROT supports routing to the following collection of large language mod
 | **Output Token Cost ($ per 1M tokens)** | 15  | 1.5  | 10   | 0.6   | 0.1  | 0.2  | 0.9  | 0.2  | 0.06  | 0.06  | 0.9  | 0.6  | 3.5  |
 
 </p>
-
-```python 
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import numpy as np
-token = 'YOUR HF TOKEN'
-nput_counter = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B", token='')
-
-tokenizer = AutoTokenizer.from_pretrained('roberta-base')
-
-score_predictor =  AutoModelForSequenceClassification.from_pretrained('CARROT-LLM-Routing/Performance', 
-                                                           problem_type="multi_label_classification", 
-                                                           num_labels=len(COSTS),
-                                                           )
-output_counter = AutoModelForSequenceClassification.from_pretrained('CARROT-LLM-Routing/Cost', 
-                                                           problem_type="regression", 
-                                                           num_labels=len(COSTS))
-def CARROT(prompts, mu, input_counter=input_counter, predictors = [score_predictor, output_counter], tokenizer=tokenizer, costs=COSTS):
-  tokenized_text = tokenizer(prompts,
-                             truncation=True,
-                             padding=True,
-                             is_split_into_words=False,
-                             return_tensors='pt')
-  input_counter.pad_token = tokenizer.eos_token
-  scores = 1/(1+np.exp(-predictors[1](tokenized_text["input_ids"]).logits.detach().numpy()))
-  output_tokens = predictors[1](tokenized_text["input_ids"]).logits.detach().numpy()
-  input_tokens = [input_counter(prompt, return_tensors="pt")["input_ids"].shape[1] for prompt in prompts]
-  input_tokens = np.array(input_tokens).T
-  costs = []  
-  for i, m in enumerate(COSTS.keys()):
-      costs.append((input_tokens*COSTS[m][0]/(1000000)+output_tokens[:,i]*COSTS[m][1]/1000).tolist())
-  costs = np.array(costs).T
-  model_idx = ((1 - mu) * scores - mu * costs*100 ).argmax(axis = 1, keepdims = True)
-  called = [id2label[idx[0]] for idx in model_idx]
-  return called
-```