misteral

selection.py

@@ -4,7 +4,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 from datasets import load_dataset
 from transformers import AutoTokenizer, AutoModelForCausalLM
-
+my_token = os.getenv('my_repo_token')
 def find_most_relevant_context(contexts, question, max_features=10000):
     # Vectorize contexts and question with limited features
     tfidf_vectorizer = TfidfVectorizer(max_features=max_features)
@@ -35,8 +35,8 @@ instruction = most_relevant_context[:300] + " " + Question
 
 # Load Mistral model and tokenizer
 model_name = "mistralai/Mistral-7B-v0.1"  # Replace with the appropriate Mistral model name if available
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name , token= my_token)
+model = AutoModelForCausalLM.from_pretrained(model_name, token= my_token)
 
 
 # Tokenize the input