Create README.md

README.md

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-title: GEPA Prompt Tuner
-emoji: 🦀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.38.2
-app_file: app.py
-pinned: false
 license: mit
+title: GEPA Prompt Tuner
+emoji: 🏢
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+title: GEPA Prompt Optimizer emoji: 🧬🤖 colorFrom: blue colorTo: green sdk: gradio sdk_version: 4.31.0 python_version: 3.10 app_file: app.py pinned: false license: mit
+GEPA Prompt Optimizer for Hugging Face Models
+This Space is a functional implementation of the GEPA (Genetic-Pareto) framework, as described in the paper "GEPA: REFLECTIVE PROMPT EVOLUTION CAN OUTPERFORM REINFORCEMENT LEARNING".
+
+It allows you to automatically optimize a prompt for a target model (like google/gemma-2b-it) by having it "learn" from feedback on a small training set.
+
+🧬 How It Works
+The application uses an evolutionary approach to refine a "seed" prompt over several iterations:
+
+Selection: It selects a promising prompt from its current pool of candidates using a Pareto-based strategy, which favors diversity.
+
+Rollout: It runs the selected prompt on a task using your target Hugging Face model (e.g., Gemma).
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+Reflection: It uses a powerful "reflector" model (Google's Gemini 1.5 Flash) to analyze the prompt's performance, the output, and detailed feedback.
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+Mutation: The reflector model proposes a new, improved prompt designed to fix the observed failures.
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+Evaluation: The new prompt is evaluated, and if it shows improvement, it is added to the candidate pool.
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+This cycle repeats until the defined "rollout budget" is exhausted, leaving you with the best-performing prompt.
+
+🚀 How to Use
+Provide API Keys:
+
+Hugging Face API Token: Enter your Hugging Face token. This is used to run inference on the target model you want to optimize for.
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+Google Gemini API Key: Enter your Gemini API key. This is required for the powerful "reflection" step. You can get a key from Google AI Studio.
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+Configure the Optimization:
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+Target Model ID: The Hugging Face model you want to create a prompt for (e.g., google/gemma-2b-it).
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+Initial Seed Prompt: The starting prompt. Your goal is to improve this!
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+Training Data: A small JSON dataset with input fields and evaluation criteria (e.g., expected_keywords). You must adapt this for your specific task.
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+Budget: The total number of times the target model will be called. Higher budgets allow for more refinement but take longer.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Start Optimization: Click the button and watch the logs to see the evolutionary process in action! The best prompt found will update in real-time.