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app (4).py

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+
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer,pipeline
+
+model_id = str(st.text_input("Enter model_id"))
+#model_id = "meta-llama/Meta-Llama-3-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    load_in_4bit=True,
+    #attn_implementation="flash_attention_2", # if you have an ampere GPU
+)
+
+max_new_tokens=100
+top_k=50
+temperature=0.1
+max_new_tokens = st.text_input("Enter max_new_tokens")
+top_k =  st.text_input("Enter max_new_tokens")
+temperature = st.text_input("Enter temperature")
+
+query = st.chat_input("Enter your query")
+st.write(query)
+
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=100, top_k=50, temperature=0.1)
+llm = HuggingFacePipeline(pipeline=pipe)
+st.write(llm.invoke(query))

requirements.txt

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+huggingface_hub==0.22.2
+streamlit
+transformers
+torch