Architecture
data/models.yaml. Every label is auditable
against the model's sources.
Specs
- Architecture
- dense
- Total params
- not disclosed
- Active params
- 3.8B
- Context window
- 128K tokens
- Attention
- gqa
- Position encoding
- rope
- Pretraining tokens
- 5.0T
- Training hardware
- A100
- Post-training
- sft, dpo
- OSI-approved
- yes
- Data released
- no
- Training code
- not released
Benchmarks
Each score carries the date it was published; we never infer or interpolate missing scores.
General reasoning
| MMLU | 67.3 | as of 2025-02-26 | source ↗ |
Math
| MATH | 64.0 | as of 2025-02-26 | source ↗ |
Available quantizations
GGUF llama.cpp's container; the common local format, k-quants from Q2 to Q8.
runs on llama.cpp, Ollama
FP8 8-bit float, frequently a native release on Hopper / Blackwell GPUs.
runs on vLLM, SGLang, TensorRT-LLM
Verified via the Hugging Face model tree ↗. Community quantizations change over time; the families shown are those with published weights at audit time.
Notable innovations
- · 200K vocab for multilingual coverage
- · 128K context at 3.8B
- · MIT-licensed Phi entry
Lineage
Small dense Phi-4 sibling; 200K vocab is the key tokenizer change.
Derived from
Phi-4 2024-12-12