Phi-4

Cost & access

Phi-4 weights are available under MIT. Direct API cost may be $0 if you self-host; budget for GPUs, storage, and engineering instead. Hosted endpoints (Together, Fireworks, Groq, etc.) charge per token—shop providers for phi-4 latency and region. With a 16K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Small open model focused on reasoning quality relative to its size.
• Modalities: text · License: MIT · Released 2024-12-12.
• Best-fit workflows for this model:
• • Drafting, summarization, and structured extraction from long documents.
• • On-prem or VPC deployment when data cannot leave your network.

Technical foundation

• Microsoft reports 14B parameters; training data: Synthetic + filtered web (Microsoft research).
• Context: 16K tokens. Open weights: yes.
• Phi-4 is positioned as a general-purpose model in the Microsoft lineup.

First API call

Run Phi-4 locally with Ollama or Hugging Face transformers (weights under MIT).

# Ollama (if model is published there)
# ollama run phi-4

# Or Hugging Face transformers:
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/phi-4", device_map="auto")
print(pipe("Hello from Phi-4", max_new_tokens=80)[0]["generated_text"])

Important technical topics

• Prompting Phi-4: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for Microsoft billing data."
• Temperature: use 0–0.3 for extraction and compliance on Phi-4; 0.7–1.0 for brainstorming.
• Tokens: Phi-4 bills by tokens (~¾ word each). 14B parameters affect capability; your bill is driven by context length and call volume.
• Context window (16K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for Phi-4.

Real enterprise patterns

• RAG with Phi-4: retrieve from your vector DB, cite sources in the prompt.
• Tool calling: define JSON schemas; let Phi-4 request functions, not free-form SQL.
• Eval suite: regression prompts before each model or prompt change.
• Cost routing: default to Phi-4 for hard tasks; smaller sibling model for triage.

Production & security

• Secrets: never commit keys for Phi-4; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=phi-4, tokens in/out, latency.
• GPU monitoring: VRAM, batch queue depth, and model revision hash on each deploy.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Support copilot: Phi-4 drafts replies from KB snippets.
• Contract clause extractor with human approval.
• Weekly metrics narrative from SQL + CSV exports.
• Agent that files expenses from receipt photos (if multimodal).

Suggested stack

• Language: Python 3.11+
• Model: Phi-4 via Ollama, vLLM, or Hugging Face
• Hardware: NVIDIA GPU with enough VRAM for quantization level
• API wrapper: FastAPI or LiteLLM proxy
• UI: Streamlit or Next.js for internal tools
• APIs: FastAPI
• Vector DB (RAG): Pinecone / Chroma / pgvector

Learning path

• Python basics
• HTTP/REST and environment variables
• Microsoft authentication and Phi-4 model id (phi-4)
• First successful call to Phi-4
• Prompt design and JSON / structured outputs
• RAG
• Tool use / function calling
• Evals and regression sets
• Production deploy + monitoring