Update app.py

app.py

@@ -2,17 +2,20 @@ import gradio as gr
 import torch
 
 from transformers import pipeline, AutoTokenizer, AutoModel, LlamaForCausalLM
+from peft import PeftModel
 
 #pipe = pipeline("text-generation", model="furquan/opt_2_7_b_prompt_tuned_sentiment_analysis", trust_remote_code=True, cache_dir="/local/home/furquanh/myProjects/week12/").to('cuda')
 
 # tokenizer = AutoTokenizer.from_pretrained("furquan/opt-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
 # model = AutoModel.from_pretrained("furquan/opt-1-3b-prompt-tuned-sentiment-analysis", trust_remote_code=True)
-model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", token="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")
 tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
 
+model = PeftModel.from_pretrained(model, "furquan/llama2-sentiment-prompt-tuned")
+
 
 title = "OPT-1.3B"
-description = "This demo uses meta's LLama-2-7b Causal LM as base model that was prompt tuned on the Stanford Sentiment Treebank-5 way dataset to only output the sentiment of a given text."
+description = "This demo uses meta's LLama-2-7b Causal LM as base model that was prompt tuned on the mteb/tweet_sentiment_extraction dataset to only output the sentiment of a given text."
 article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2104.08691.pdf' target='_blank'>The Power of Scale for Parameter-Efficient Prompt Tuning</a></p>"