Update README.md

README.md

@@ -29,7 +29,50 @@ pipeline_tag: text2text-generation
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+Here are the **following two steps for a quick start with model application**:
+
+
+1. Loading model and tokenizer:
+
+```python
+import torch
+from transformers import AutoTokenizer, T5ForConditionalGeneration
+
+# Setup model path.
+model_path = "nicolay-r/flan-t5-tsa-prompt-base"
+# Setup device.
+device = "cuda:0"
+
+model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.to(device)
+```
+
+2. Setup ask method for generating LLM responses:
+
+```python
+def ask(prompt):
+  inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+  inputs.to(device)
+  output = model.generate(**inputs, temperature=1)
+  return tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+```
+
+Finally, you can infer model results as follows:
+
+```python
+# Input sentence.
+sentence = "I would support him"
+# Input target.
+target = "him"
+# output response
+flant5_response = ask(f"What's the attitude of the sentence '{context}', to the target '{target}'?")
+print(f"Author opinion towards `{target}` in `{sentence}` is:\n{flant5_response}")
+```
+
+The response of the model is as follows:
+> Author opinion towards "him" in "I would support him despite his bad behavior." is: **positive**
 
 ### Downstream Use