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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ base_model: Qwen/Qwen3-30B-A3B
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - prefix-tuning
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+ - persona
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+ - einstein
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+ - philosophy
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+ - debate
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+ ---
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+
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+ # Einstein Prefix Adapter
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+
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+ Prefix-tuned adapter that teaches the model to embody Albert Einstein's reasoning patterns, voice, and philosophical positions.
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ from peft import PeftModel
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+
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+ base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B")
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+ model = PeftModel.from_pretrained(base_model, "debaterhub/prefix-einstein")
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+
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+ tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B")
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+ ```
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+
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+ ## Training Details
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+
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+ - **Method**: PEFT Prefix-Tuning (40 virtual tokens)
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+ - **Base Model**: Qwen/Qwen3-30B-A3B (30B MoE, 3B active)
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+ - **Dataset**: 447 examples of Einstein-style debate responses
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+ - **Epochs**: 3
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+ - **Hardware**: 8x A100-40GB
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+
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+ ## Evaluation
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+
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+ Evaluated using LLM-as-Judge (Claude Opus 4.5) on 5 dimensions:
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+ - Ideational Fidelity (35%)
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+ - Reasoning Pattern (25%)
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+ - Voice Authenticity (20%)
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+ - Engagement Quality (15%)
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+ - Anti-Patterns (5%)
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+
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+ **Baseline**: 3.3/5.0
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+ **Trained**: 3.4/5.0
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+
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+ Key improvement: Reduced meta-roleplay anti-patterns, more direct in-character responses.
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+
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+ ## Framework Versions
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+
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+ - PEFT 0.18.0
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+ - Transformers 4.46.0
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