Create README.md

README.md

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+# How to use this
+
+```
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from IPython.display import display, Markdown
+
+def make_inference(topic):
+  batch = tokenizer(f"### INSTRUCTION\nBelow summary for a blog post, please write a social media post\
+  \n\n### Topic:\n{topic}\n### Social media post:\n", return_tensors='pt')
+
+  with torch.cuda.amp.autocast():
+    output_tokens = model.generate(**batch, max_new_tokens=200)
+
+  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))
+
+if __name__=="__main__":
+
+  # Set up user name and model name
+  hf_username = "lgfunderburk"
+  model_name = 'tech-social-media-posts'
+  peft_model_id = f"{hf_username}/{model_name}"
+
+  # Apply PETF configuration, setup model and autotokenizer
+  config = PeftConfig.from_pretrained(peft_model_id)
+  model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, 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)
+
+  # Summary to generate a social media post about
+  topic = "The blog post demonstrates how to use JupySQL and DuckDB to query CSV files with SQL in a Jupyter notebook. \
+          It covers installation, setup, querying, and converting queries to DataFrame. \
+          Additionally, the post shows how to register SQLite user-defined functions (UDF), \
+          connect to a SQLite database with spaces, switch connections between databases, and connect to existing engines. \
+          It also provides tips for using JupySQL in Databricks, ignoring deprecation warnings, and hiding connection strings."
+  
+
+  # Generate social media post
+  make_inference(topic)
+
+
+```