Phi-3 / Phi-4

Phi-3 and Phi-4 are Microsoft’s high-capability small language models using a shared transformer decoder architecture (Phi3ForCausalLM). Phi-4-mini and Phi-4 achieve strong benchmark results at relatively small parameter counts.


Task	Text Generation
Architecture	`Phi3ForCausalLM`
Parameters	3.8B – 14B
HF Org	microsoft

Available Models

Phi-4: 14B
Phi-4-mini-instruct: 3.8B
Phi-3.5-mini-instruct: 3.8B
Phi-3-medium-128k-instruct: 14B
Phi-3-mini-128k-instruct: 3.8B
Phi-3-mini-4k-instruct: 3.8B

Architecture

Phi3ForCausalLM

Example HF Models

Model	HF ID
Phi-4	`microsoft/Phi-4`
Phi-4-mini-instruct	`microsoft/Phi-4-mini-instruct`
Phi-3-mini-4k-instruct	`microsoft/Phi-3-mini-4k-instruct`
Phi-3-mini-128k-instruct	`microsoft/Phi-3-mini-128k-instruct`
Phi-3-medium-128k-instruct	`microsoft/Phi-3-medium-128k-instruct`

Example Recipes

Recipe	Description
phi_4_squad.yaml	SFT — Phi-4 on SQuAD
phi_4_squad_peft.yaml	LoRA — Phi-4 on SQuAD
phi_3_mini_it_squad.yaml	SFT — Phi-3-mini Instruct on SQuAD
phi_3_mini_it_squad_peft.yaml	LoRA — Phi-3-mini Instruct on SQuAD

Try with NeMo AutoModel

1. Install (full instructions):

$ pip install nemo-automodel

2. Clone the repo to get the example recipes:

$ git clone https://github.com/NVIDIA-NeMo/Automodel.git
$ cd Automodel

3. Run the recipe from inside the repo:

$ automodel --nproc-per-node=8 examples/llm_finetune/phi/phi_4_squad.yaml

Run with Docker

1. Pull the container and mount a checkpoint directory:

$ docker run --gpus all -it --rm \
>   --shm-size=8g \
>   -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>   nvcr.io/nvidia/nemo-automodel:26.04.00

2. Navigate to the AutoModel directory (where the recipes are):

$ cd /opt/Automodel

3. Run the recipe:

$ automodel --nproc-per-node=8 examples/llm_finetune/phi/phi_4_squad.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide.