sanjusanjay
/

phi-2-startup-advisor-lora

@@ -1,199 +1,113 @@
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 ---
 library_name: transformers
+tags:
+- lora
+- peft
+- phi-2
+- startup-advisor
+- instruction-tuning
+- llm
 ---
+# 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.
+---
+### 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](https://huggingface.co/microsoft/phi-2)
+### License
+Apache 2.0 (inherits base model license)
+---
+## Model Sources
+- **Base model:** Microsoft Phi-2
+- **Fine-tuning method:** Parameter-Efficient Fine-Tuning (LoRA)
+- **Training framework:** Hugging Face Transformers + PEFT
+---
+## 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?”
+---
+### Out-of-Scope Use
+This model should **NOT** be used for:
+- Legal advice
+- Financial investment advice
+- Medical advice
+- Regulatory or compliance decisions
+---
+## 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.
+---
+## How to Get Started
+### Load the model (LoRA adapters)
+```python
+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()