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+  <b style="font-size: 40px;">SummLlama3-8B</b>
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+Are you looking for a summarizer that outperforms the nearly 10x larger Llama3-70B-Instruct while offering much faster inference speed? 
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+Our **SummLlama3-8B** could be exactly what you need!
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+SummLlama3 is initialized from Llama3-8B-Instruct, with additional training using Direct Preference Optimization (DPO) based on human-like summarization feedback. 
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+Please refer to [our paper](link) to catch up how to exploit LLM-generated feedback in the context of text summarization.
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+We also released a larger model, **SummLlama3-70B**. Please go to the [Huggingface link](link) for this model.
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+Here is a brief overview of our summarizer:
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+Rather than relying on expensive human feedback, we utilize high-quality, multi-dimensional, and fine-grained feedback generated by large language models (LLMs).
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+This model excels at **faithfulness**, **completeness**, and **conciseness**, which are the three human-preferred aspects to judge what is a good summarizer.
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+- Faithfulness: a summarizer does not manipulate the information in the input text and add any information not directly inferable from the input text.
+- Completeness: a summarizer ensures the inclusion of all key information from the input text in the output summary.
+- Conciseness: a summarizer refrains from incorporating information outside the key information in the output, maintaining a succinct and focused summary.
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+Based on our comprehensive evaluation, which included both human and automated assessments of summary quality, SummLlama3 demonstrated significant improvements over the original Llama3 series.
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+Here is the results:
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+## Human Evaluation
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+## Autoamted Evaluation using [FineSurE](link)
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