Spaces:
Sleeping
Sleeping
Feat: add task synonyms
Browse files
app.py
CHANGED
@@ -1,5 +1,19 @@
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import gradio as gr
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# LLM performance data with scores
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performance_data = {
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"Undergraduate level knowledge": [("Claude 3 Opus", 86.8), ("GPT-4", 86.4), ("Gemini 1.0 Ultra", 83.7)],
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@@ -15,9 +29,18 @@ performance_data = {
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}
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def recommend_llm(task):
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return "No data available"
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recommendations_sorted = sorted(recommendations, key=lambda x: x[1], reverse=True)
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result = f"For {task}, the recommended LLMs are:\n"
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for i, (model, score) in enumerate(recommendations_sorted):
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@@ -27,11 +50,11 @@ def recommend_llm(task):
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# Gradio interface
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interface = gr.Interface(
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fn=recommend_llm,
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inputs=gr.
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outputs=gr.Textbox(label="LLM Recommendations"),
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title="LLM Recommendation App",
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description="
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)
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# Launch the app
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interface.launch()
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import gradio as gr
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# Synonyms for each task category
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task_synonyms = {
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"Undergraduate level knowledge": ["undergraduate level knowledge", "MMLU"],
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"Graduate level reasoning": ["graduate level reasoning", "GPOA", "Diamond"],
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"Grade school math": ["grade school math", "GSM8K"],
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"Math problem-solving": ["math problem-solving", "MATH"],
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"Multilingual math": ["multilingual math", "MGSM"],
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"Code": ["code", "coding", "programming", "HumanEval"],
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"Reasoning over text": ["reasoning over text", "DROP", "F1 score"],
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"Mixed evaluations": ["mixed evaluations", "BIG-Bench-Hard"],
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"Knowledge Q&A": ["knowledge Q&A", "ARC-Challenge"],
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"Common Knowledge": ["common knowledge", "HellaSwag"],
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}
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# LLM performance data with scores
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performance_data = {
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"Undergraduate level knowledge": [("Claude 3 Opus", 86.8), ("GPT-4", 86.4), ("Gemini 1.0 Ultra", 83.7)],
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}
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def recommend_llm(task):
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# Normalize the input task to match against synonyms
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task_lower = task.lower()
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main_category = None
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for key, synonyms in task_synonyms.items():
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if task_lower in map(str.lower, synonyms):
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main_category = key
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break
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if not main_category:
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return "No data available"
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recommendations = performance_data.get(main_category, [])
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recommendations_sorted = sorted(recommendations, key=lambda x: x[1], reverse=True)
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result = f"For {task}, the recommended LLMs are:\n"
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for i, (model, score) in enumerate(recommendations_sorted):
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# Gradio interface
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interface = gr.Interface(
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fn=recommend_llm,
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inputs=gr.Textbox(label="Enter Task"),
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outputs=gr.Textbox(label="LLM Recommendations"),
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title="LLM Recommendation App",
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description="Enter a task to get recommendations for the best LLMs based on performance data. For example, you can enter 'coding', 'undergraduate level knowledge', etc."
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)
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# Launch the app
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interface.launch()
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