Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+---
+
+# test of ModernBERT2Olmo-large_1b
+
+experimental seq2seq with EncoderDecoderModel. You will need to patch `modeling_llama.py` with [this code](https://gist.github.com/pszemraj/a15219f33d94dc53a6e270c0c81360ec) for it work
+
+> [!WARNING]
+> WIP + output of this model is gibberish bc cross attn needs training
+
+```py
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+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)
+```
+

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+}

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special_tokens_map.json

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tokenizer_config.json

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