add interface

app.py

@@ -1,4 +1,21 @@
 import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Set the title of the app
+st.title('LLaMA2Glenda')
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("tminh/llama-2-7b-glenda")
+tokenizer = AutoTokenizer.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded")
+
+# Create a text input for the prompt
+prompt = st.text_input('Enter your prompt:')
+
+# Create a button to trigger the inference
+if st.button('Generate Answer'):
+    # Run text generation pipeline
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+    result = pipe(f"<s>[INST] {prompt} [/INST]")
+    # Display the result
+    st.write(result[0]['generated_text'])
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)

llama_2_inference.py

@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+"""Llama 2 Inference.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1tS9ep-M5slbxKrGP2btamFUhMM00QkKt
+
+# Fine-tune Llama 2 in Google Colab
+> 🗣️ Large Language Model Course
+
+❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne), based on Younes Belkada's [GitHub Gist](https://gist.github.com/younesbelkada/9f7f75c94bdc1981c8ca5cc937d4a4da). Special thanks to Tolga HOŞGÖR for his solution to empty the VRAM.
+
+This notebook runs on a T4 GPU. (Last update: 24 Aug 2023)
+"""
+
+!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7
+
+import os
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+)
+from peft import LoraConfig, PeftModel
+from trl import SFTTrainer
+
+model = AutoModelForCausalLM.from_pretrained("tminh/llama-2-7b-glenda")
+
+model_name = "TinyPixel/Llama-2-7B-bf16-sharded"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Ignore warnings
+logging.set_verbosity(logging.CRITICAL)
+
+# Run text generation pipeline with our next model
+prompt = "What can drug D07OAC do?"
+pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+result = pipe(f"<s>[INST] {prompt} [/INST]")
+print(result[0]['generated_text'])
+