Create prompt-model.py

prompt-model.py

@@ -0,0 +1,44 @@
+pip -q install langchain huggingface_hub transformers sentence_transformers accelerate bitsandbytes
+
+import os
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = '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'
+
+from langchain import PromptTemplate, HuggingFaceHub, LLMChain
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+
+prompt = PromptTemplate(template=template, input_variables=["question"])
+
+llm_chain = LLMChain(prompt=prompt, 
+                     llm=HuggingFaceHub(repo_id="google/flan-t5-xl", 
+                                        model_kwargs={"temperature":0, 
+                                                      "max_length":64}))
+
+
+from langchain.llms import HuggingFacePipeline
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_id = 'Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_id, from_tf=True)
+
+pipeline = pipeline(
+    "text2text-generation",
+    model=model, 
+    tokenizer=tokenizer, 
+    max_length=128
+)
+
+local_llm = HuggingFacePipeline(pipeline=pipeline)
+
+
+llm_chain = LLMChain(prompt=prompt, 
+                     llm=local_llm
+                     )
+
+question = "Excel Sheet"
+
+print(llm_chain.run(question))