Phi-4-mini-reasoning
Phi-4-mini-reasoning is a 3.8B-parameter open-source LLM from Microsoft designed for mathematical reasoning and multi-step logic problems in resource-constrained environments. It achieves strong performance on math benchmarks (AIME: 57.5, MATH-500: 94.6) through distillation from DeepSeek-R1 synthetic data. Licensed under MIT with no gating, it supports 128K context and is suitable for education, embedded tutoring, and edge deployment.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | microsoft |
| Parameters | 3.8B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 36.8k |
| Likes | 240 |
| Last updated | 2025-12-10 |
| Source | microsoft/Phi-4-mini-reasoning |
What Phi-4-mini-reasoning is
Dense decoder-only Transformer with 3.8B parameters, 200K vocabulary, grouped-query attention, and shared input/output embeddings. Trained on 150B tokens of synthetic math data (30B tokens verified correct solutions) over 2 days on 128 H100s. Supports English, targets chat-based prompts, integrates with transformers 4.51.3+, and requires flash_attn 2.7.4+ and torch 2.5.1.
Run Phi-4-mini-reasoning locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="microsoft/Phi-4-mini-reasoning")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Inference: ~7.7 GB VRAM (estimated for fp16 with 128K context; bf16 ~15.4 GB). Example code uses device_map='cuda' and torch_dtype='auto'. Training: 128× H100-80GB (reference). Quantization (int8/int4) via bitsandbytes or GPTQ reduces memory further; LoRA fine-tuning feasible on consumer GPUs with lower rank.
No explicit LoRA/QLoRA guidance in card. Model card mentions placeholder tokens for 'downstream fine-tuning' (vocabulary extendable to 200K). Stack includes transformers 4.51.3+, compatible with standard LoRA libraries (peft). Full fine-tuning on math domain data likely effective given synthetic-data training approach; limited guidance on adaptation to other domains.
When to avoid it — and what to weigh
- General-Purpose Conversational AI — Model is specifically designed and evaluated for math reasoning only. Card states it is 'not specifically designed or evaluated for all downstream purposes.' Use for non-math tasks risks poor performance and safety issues.
- Factual Knowledge or World Understanding — Card explicitly notes 'the model simply does not have the capacity to store too much factual knowledge, therefore, users may experience factual incorrectness.' Not suitable for current events, trivia, or knowledge-heavy domains without RAG.
- Multilingual or Non-English Use Cases — Training data is English-only; card lists 'Supported languages: English' with no multilingual evaluation. Performance on non-English math problems is unknown and likely degraded.
- High-Risk Compliance Scenarios Without Review — Card advises: 'Developers should evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.' Financial, medical, or legal math applications require thorough validation.
License & commercial use
Released under MIT License, a permissive OSI-approved license. No gating. Allows commercial use, modification, and distribution with attribution and no warranty.
MIT is a permissive OSI license explicitly permitting commercial use. Model itself is open-source with no gating restrictions. However, card states: 'Developers should be aware of and adhere to applicable laws or regulations (including but not limited to privacy, trade compliance laws, etc.) relevant to their use case.' This means: license allows commercial deployment, but you bear responsibility for compliance (data privacy, domain-specific regulation, output accuracy). No explicit guarantee of support or indemnification from Microsoft; use MSRC process for security issues.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM risks apply: no evaluation of adversarial robustness, prompt injection, or jailbreak resistance stated. Model trained on synthetic data; lower risk of memorized PII but no privacy analysis provided. Card advises: 'developers should consider common limitations of language models' and evaluate for 'accuracy, safety, and fairness.' No independent security audit or red-team results disclosed. Recommend: input validation, output filtering, and domain-specific testing for production use.
Alternatives to consider
DeepSeek-R1-Distill-Qwen-7B
Larger (7B), higher AIME (53.3) and MATH-500 (91.4) scores; requires more VRAM but better reasoning depth. Uses same distillation approach from R1.
Llama-3.2-3B-Instruct
Similar parameter count (3B), lower reasoning scores (AIME 6.7) but general-purpose design. Better for non-math tasks; less specialized than Phi-4-mini-reasoning.
Phi-4-mini-instruct
Same 3.8B architecture but instruction-tuned for general tasks, not math-specialized. Lower reasoning scores; trade-off: broader capability for less focused reasoning.
Ship Phi-4-mini-reasoning with senior software developers
Phi-4-mini-reasoning offers strong math problem-solving in a compact, permissively-licensed package. Start with the Hugging Face model, try inference in Azure AI Studio, or consult our custom LLM team to integrate it into your product. Verify accuracy and compliance for your use case before production.
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Phi-4-mini-reasoning FAQ
Can I use this model commercially?
What GPU VRAM do I need?
Does it work for non-math tasks?
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Software developers & web developers for hire
DEV.co helps companies turn open-source tools like Phi-4-mini-reasoning into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy Math Reasoning in Your App?
Phi-4-mini-reasoning offers strong math problem-solving in a compact, permissively-licensed package. Start with the Hugging Face model, try inference in Azure AI Studio, or consult our custom LLM team to integrate it into your product. Verify accuracy and compliance for your use case before production.