Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+# Title for your app
+st.title("Llama-3-8B-Physics Master - Model Inference")
+
+# Load the model and tokenizer from Hugging Face
+@st.cache_resource
+def load_model():
+    model = AutoModel.from_pretrained("gallen881/Llama-3-8B-Physics_Master-GGUF")
+    tokenizer = AutoTokenizer.from_pretrained("gallen881/Llama-3-8B-Physics_Master-GGUF")
+    return model, tokenizer
+
+# Load the model once and store it in cache
+model, tokenizer = load_model()
+
+# Text input for the user
+user_input = st.text_area("Enter your input here:")
+
+if st.button("Generate Output"):
+    if user_input:
+        # Tokenize the input
+        inputs = tokenizer(user_input, return_tensors="pt")
+        
+        # Forward pass through the model
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Get the output embeddings or logits (depending on the model structure)
+        # For example, let's say we want to display embeddings
+        st.write("Model Output Embeddings:", outputs.last_hidden_state)
+
+    else:
+        st.write("Please enter some input.")