Create main.py

main.py

@@ -0,0 +1,31 @@
+from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline,BitsAndBytesConfig
+import accelerate
+import bitsandbytes
+from langchain_core.prompts import PromptTemplate
+
+quants = BitsAndBytesConfig(load_in_4bit=True)
+model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+tokenizer = AutoTokenizer.from_pretrained(model_id,quantization_config=quants)
+model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=quants)
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+hf = HuggingFacePipeline(pipeline=pipe)
+
+def generate_blog(role , words , topic):
+    template = ''' You are an expert Blog generator , Given the Topic , the intended audience and the maximum number of words , 
+    Write a blog on the given topic 
+    
+    Topic : {topic}
+    Intended Audince : {role}
+    Number of Words : {words}
+    
+    Strictly return the output in a markdown format'''
+
+    
+    prompt = PromptTemplate.from_template(template)
+    
+    chain = prompt | hf
+    
+    
+    
+    return chain.invoke({"topic": topic,"words":words,"role":role})