phi-4

About

What phi-4 is

Phi-4 Model Card Phi-4 Technical Report Model Summary Intended Use Data Overview Training Datasets Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.). 3. Acquired academic books and Q&A datasets. 4. High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. Benchmark datasets We evaluated phi-4 using OpenAI’s SimpleEval and our own internal benchmarks to understand the model’s capabilities, more specifically: MMLU: Popular aggregated dataset for multitask language understanding. MATH: Challenging competition math problems. GPQA: Complex, graduate-level science questions. DROP: Complex comprehension and reasoning. MGSM: Multi-lingual grade-school math. HumanEval: Functional code generation. SimpleQA: Factual responses. Safety Approach phi-4 has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated synthetic datasets. The overall technique employed to do the safety alignment is a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization), including publicly available datasets focusing on helpfulness and harmlessness as well as various questions and answers targeted to multiple safety categories. Safety Evaluation and Red-Teaming Prior to release, phi-4 followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversat

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys phi-4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (phi-4 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"phi-4","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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