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references
Browse files- app.py +4 -1
- constants.py +1 -1
app.py
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@@ -54,7 +54,10 @@ def make_clickable_model(model_name, model_info):
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link = model_info[model_name]['hf_name']
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else:
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link = f"https://huggingface.co/{model_info[model_name]['hf_name']}"
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def build_demo(original_df, TYPES):
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link = model_info[model_name]['hf_name']
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else:
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link = f"https://huggingface.co/{model_info[model_name]['hf_name']}"
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if model_name.startswith("gpt"):
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return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted; background-color: lightgray;">{model_info[model_name]["pretty_name"]}</a>'
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else:
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return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_info[model_name]["pretty_name"]}</a>'
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def build_demo(original_df, TYPES):
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constants.py
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@@ -16,7 +16,7 @@ TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body>
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INTRODUCTION_TEXT= """
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# URIAL Bench (Evaluating Base LLMs with URIAL on MT-Bench)
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[🛜 Website](https://allenai.github.io/re-align/index.html) | [💻 GitHub](https://github.com/Re-Align/URIAL) | [📖 Paper](https://arxiv.org/abs/2312.01552) | [🐦
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> URIAL Bench tests the capacity of base LLMs for alignment without introducing the factors of fine-tuning (learning rate, data, etc.), which are hard to control for fair comparisons.
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Specifically, we use [URIAL](https://github.com/Re-Align/URIAL/tree/main/run_scripts/mt-bench#run-urial-inference) to align a base LLM, and evaluate its performance on MT-Bench.
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INTRODUCTION_TEXT= """
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# URIAL Bench (Evaluating Base LLMs with URIAL on MT-Bench)
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+
[🛜 Website](https://allenai.github.io/re-align/index.html) | [💻 GitHub](https://github.com/Re-Align/URIAL) | [📖 Paper](https://arxiv.org/abs/2312.01552) | [🐦 Tweet 1](https://x.com/billyuchenlin/status/1759541978881311125?s=20) | [🐦 Tweet 2](https://x.com/billyuchenlin/status/1762206077566013505?s=20)
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> URIAL Bench tests the capacity of base LLMs for alignment without introducing the factors of fine-tuning (learning rate, data, etc.), which are hard to control for fair comparisons.
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Specifically, we use [URIAL](https://github.com/Re-Align/URIAL/tree/main/run_scripts/mt-bench#run-urial-inference) to align a base LLM, and evaluate its performance on MT-Bench.
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