initial commit

.gitignore

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+/venv
+.env

app.py

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+import streamlit as st
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline
+import os
+import logging, sys
+from dotenv import load_dotenv
+
+from huggingface_hub import login
+#load_dotenv()
+
+#HF_TOKEN  = os.environ.get("HF_API_TOKEN")
+HF_TOKEN = st.secrets["HF_API_TOKEN"]
+login(token=HF_TOKEN)
+
+# Setup logging
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+ 
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+
+#tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
+#model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
+
+
+# Define the directory to save the model
+#save_directory = "models"
+
+# Save the tokenizer and model to the specified directory
+#Run once
+#model.save_pretrained(save_directory)
+#tokenizer.save_pretrained(save_directory)
+
+# Load the tokenizer and model from the saved directory
+#tokenizer = AutoTokenizer.from_pretrained(save_directory, local_files_only=True,load_in_8bit=True,)
+#model = AutoModelForCausalLM.from_pretrained(save_directory, local_files_only=True,load_in_8bit=True)    
+
+
+
+
+pipe = pipeline("text-generation", 
+                model=model,#"mistralai/Mistral-7B-v0.1",
+                tokenizer=tokenizer,
+                )
+
+# Generate text using the pipeline
+result = pipe("tell me about transformer.", max_length=50, truncation=True)
+print(result)
+
+#Using  mistralai/Mistral-7B-Instruct-v0.2
+
+#save_directory = 'Mistral-7B-Instruct-v0.2'
+
+#tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
+#model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") 
+
+
+#tokenizer = AutoTokenizer.from_pretrained(save_directory, local_files_only=True,load_in_8bit=True,)
+#model = AutoModelForCausalLM.from_pretrained(save_directory, local_files_only=True,load_in_8bit=True)
+
+
+pipe = pipeline("text-generation",  
+                model=model,        #'Mistral-7B-Instruct-v0.2'
+                tokenizer=tokenizer,
+                )
+
+
+question =st.text_input("enter your question","tell me about transformer.") 
+
+# Generate text using the pipeline
+result = pipe(question, max_length=50, truncation=True)
+print(result)