initial version

app.py

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+import streamlit as st
+from transformers import pipeline
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+peft_model_id = "JackLiuAngel/bloom-7b1-lora-alfred-team-20240730"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+# sentiment_pipeline = pipeline("sentiment-analysis")
+
+st.title("Team info finetuned in bigscience/bloom-7b1")
+st.write("ask a question about our team:")
+
+user_input = st.text_input("")
+
+
+
+if user_input:
+    batch = tokenizer(f"“{user_input}” ->: ", return_tensors='pt')
+
+    with torch.cuda.amp.autocast():
+        output_tokens = model.generate(**batch, max_new_tokens=50)
+
+    # print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
+
+
+    result = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+
+    st.write(f"reply: {result}")

requirements.txt

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+streamlit
+transformers
+torch