Model reference · open weights
Phi-4-mini is an open-weight language model from microsoft, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
🎉Phi-4: [mini-reasoning | reasoning] | [multimodal-instruct | onnx]; [mini-instruct | onnx] Model Summary Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures. 📰 Phi-4-mini Microsoft Blog <br 📖 Phi-4-mini Technical Report <br 👩🍳 Phi Cookbook <br 🏡 Phi Portal <br 🖥️ Try It Azure, Huggingface <br 🚀 Model paper Intended Uses Primary Use Cases The model is intended for broad multilingual commercial and research use. The model provides uses for general purpose AI systems and applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially math and logic). The model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. Use Case Considerations The model is not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models, as well as performance difference across languages, as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including but not limited to privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. Release Notes This release of Phi-4-mini-instruct is based on valuable user feedback from the Phi-3 series. The Phi-4-mini model employed new architecture for efficiency, larger vocabulary for multilingual support, and better post-training techniques were used for instruction following, function calling, as well as a
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | microsoft |
|---|---|
| Type | Language models |
| Parameters (lead) | 3.8B |
| Variants | 1 |
| Runs with | transformers |
| Released | 2025-02-19 |
| Popularity | 458k downloads / month |
| Likes | 826 |
| Licence | Open weights |
How it works
Variants
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.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| Phi-4-mini-instruct | 3.8B | BF16 | ~8.8 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys phi-4-mini for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (phi-4-mini 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-mini","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗