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kendrickfff/ai-pm-ken-advisor
ai-pm-ken-advisor is a machine learning model from kendrickfff. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
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Updated Oct 20, 2025
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From the Hugging Face model README
Model name: ken-fine-tuned-phi2
Base model: microsoft/phi-2
Architecture: Decoder-only Transformer (Causal LM)
Fine-tuning framework: LoRA (Low-Rank Adaptation) with PEFT
Dataset type: Instruction-style chat pairs for Product Management scenarios
Ken Advisor is a fine-tuned version of Microsoft’s Phi-2, trained to serve as an AI Product Management Assistant. It provides structured, concise, and context-aware guidance across various product management tasks — from ideation and discovery to stakeholder communication and AI integration.
This model was trained using a LoRA adaptation, allowing efficient fine-tuning on consumer GPUs while preserving Phi-2’s original reasoning strength.
| Parameter | Value |
|---|---|
| Base model | microsoft/phi-2 |
| Method | LoRA fine-tuning (PEFT) |
| Dataset size | ~540 samples |
| Epochs | 3 |
| Batch size | 1 |
| Learning rate | 2e-4 |
| Max sequence length | 512 |
| Average training loss | 0.2468 |
| Framework | Hugging Face Transformers (v4.38) |
from transformers import pipeline
pipe = pipeline("text-generation", model="your-username/ken-fine-tuned-phi2")
prompt = "User: How should I prioritize AI features in my product roadmap?\nAssistant:"
result = pipe(prompt, max_new_tokens=150)
print(result[0]["generated_text"])
This model is designed for:
⚠️ Limitations: Not suitable for legal, financial, or medical advice. Outputs may include hallucinations or inaccuracies — always verify before use.
If you use this model, please cite:
@model{ken_advisor_phi2,
title={Ken Advisor — Fine-Tuned Phi-2 for Product Management},
author={Kendrick F.},
year={2025},
base_model={microsoft/phi-2},
framework={Transformers + PEFT}
}