Update README.md

README.md

@@ -10,8 +10,27 @@ language:
 experimental seq2aeq with EncoderDecoderModel. You will need to patch `modeling_llama.py` with [this code](https://gist.github.com/pszemraj/a15219f33d94dc53a6e270c0c81360ec) for it work
 
 ```py
-# Use a pipeline as a high-level helper
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-pipe = pipeline("text2text-generation", model="pszemraj/ModernBERT2Olmo-large_1b-test")
+tokenizer = AutoTokenizer.from_pretrained("pszemraj/ModernBERT2Olmo-large_1b-test")
+model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/ModernBERT2Olmo-large_1b-test")
+
+ARTICLE_TO_SUMMARIZE = (
+    "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
+    "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
+    "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
+)
+prompt = f"summarize dis botmon: {ARTICLE_TO_SUMMARIZE}"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# autoregressively generate summary (uses greedy decoding by default)
+generated_ids = model.generate(
+    **inputs,
+    min_new_tokens=10,
+    max_new_tokens=100,
+)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_text)
 ```
+
+Output is currently gibberish bc cross attn needs training