Update README.md

README.md

@@ -12,16 +12,41 @@ pipeline_tag: text-generation
  fine-tuned from [sea-lion-7b-instruct](aisingapore/sea-lion-7b-instruct) with question-pandas expression pairs.
 
 ## How to use:
-  ```python
+  ```python  
   from transformers import AutoModelForCausalLM, AutoTokenizer
-  from peft import PeftModel
+  import pandas as pd
   
-  base_model = "aisingapore/sea-lion-7b-instruct"
-  adapter_model = "AIAT/Optimizer-sealion2pandas"
+  tokenizer = AutoTokenizer.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)
+  model = AutoModelForCausalLM.from_pretrained("aisingapore/sea-lion-7b-instruct", trust_remote_code=True)
+
+  df = pd.read_csv("Your csv..")
+  
+  prompt_template = "### USER:\n{human_prompt}\n\n### RESPONSE:\n"
+  
+  prompt = """\
+  You are working with a pandas dataframe in Python. 
+  The name of the dataframe is `df`.
+  This is the result of `print(df.head())`:
+  {df_str}
   
-  model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True)
-  model = PeftModel.from_pretrained(model, adapter_model, trust_remote_code=True)
-  tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+  Follow these instructions: 
+  1. Convert the query to executable Python code using Pandas. 
+  2. The final line of code should be a Python expression that can be called with the `eval()` function.
+  3. The code should represent a solution to the query.
+  4. PRINT ONLY THE EXPRESSION.
+  5. Do not quote the expression.
+  Query: {query_str} """
+  
+  def create_prompt(query_str, df):
+      text = prompt.format(df_str=str(df.head()), query_str=query_str)
+      text = prompt_template.format(human_prompt=text)
+      return text
+  
+  full_prompt = create_prompt("Find test ?", df)
+
+  tokens = tokenizer(full_prompt, return_tensors="pt")
+  output = model.generate(tokens["input_ids"], max_new_tokens=20, eos_token_id=tokenizer.eos_token_id)
+  print(tokenizer.decode(output[0], skip_special_tokens=True))
   ```
 
 # sponser