sanjusanjay/phi-2-startup-advisor-lora

Phi-2 Startup Advisor (LoRA)

Model Details

Model Description

This model is a LoRA fine-tuned version of Microsoft Phi-2, adapted to act as a startup advisor.
It provides structured, actionable guidance to early-stage founders by learning reasoning patterns from real-world startup case studies.

The model focuses on:

• Monetization strategy
• Cash burn reduction
• Strategic pivots
• Niche targeting
• Data-driven decision-making

This repository contains only LoRA adapters, not the full base model.

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Developed by

Sanjay (independent project)

Model type

Decoder-only causal language model (instruction-tuned via LoRA)

Language(s)

English

Finetuned from model

microsoft/phi-2

License

Apache 2.0 (inherits base model license)

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Model Sources

• Base model: Microsoft Phi-2
• Fine-tuning method: Parameter-Efficient Fine-Tuning (LoRA)
• Training framework: Hugging Face Transformers + PEFT

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Uses

Direct Use

This model is intended to be used as:

• A startup advisory chatbot
• A decision-support assistant for early-stage founders
• A reasoning-focused LLM for business strategy discussions

Example Use Cases

• “My startup is burning cash. How can I reach profitability?”
• “Should I target a niche or go mass-market?”
• “How can I pivot when my current model is failing?”

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Out-of-Scope Use

This model should NOT be used for:

• Legal advice
• Financial investment advice
• Medical advice
• Regulatory or compliance decisions

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Bias, Risks, and Limitations

• The model is trained on a limited number of startup case studies, which may bias it toward patterns common in Indian and SaaS/fintech startups.
• It may generate overly optimistic strategies if used without external validation.
• It does not have real-time market awareness.
• Responses should be treated as advisory insights, not authoritative decisions.

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How to Get Started

Load the model (LoRA adapters)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/phi-2",
    load_in_4bit=True,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(
    base_model,
    "sanjusanjay/phi-2-startup-advisor-lora"
)

tokenizer = AutoTokenizer.from_pretrained(
    "sanjusanjay/phi-2-startup-advisor-lora"
)

model.eval()