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pytorch/
Phi-4-mini-instruct-INT8-INT4

Name: Phi-4-mini-instruct-INT8-INT4
Availability: InStock

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Phi4-mini is quantized by the PyTorch team using torchao with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (INT8-INT4). The model is suitable for mobile deployment with ExecuTorch.

We provide the quantized pte for direct use in ExecuTorch. (The provided pte file is exported with at max_seq_length/max_context_length of 1024; if you wish to change this, re-export the quantized model following the instructions in Exporting to ExecuTorch.)

Running in a mobile app

The pte file can be run with ExecuTorch on a mobile phone. See the instructions for doing this in iOS. On iPhone 15 Pro, the model runs at 17.3 tokens/sec and uses 3206 Mb of memory.

⚠️ Caveat: Our mobile demo apps have regressed support for the Phi-4 tokenizer, so this model will not currently run in our official apps. If you are using your own app and runner, you can still load and run the .pte file successfully. See https://github.com/pytorch/executorch/issues/14077 for details and tracking.

Quantization Recipe

First need to install the required packages:

Shell

pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao torch --index-url https://download.pytorch.org/whl/nightly/cu126

Untie Embedding Weights

We want to quantize the embedding and lm_head differently. Since those layers are tied, we first need to untie the model:

from transformers import (
  AutoModelForCausalLM,
  AutoProcessor,
  AutoTokenizer,
)
import torch

model_id = "microsoft/Phi-4-mini-instruct"
untied_model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

print(untied_model)
from transformers.modeling_utils import find_tied_parameters
print("tied weights:", find_tied_parameters(untied_model))
if getattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings"):
    setattr(untied_model.config.get_text_config(decoder=True), "tie_word_embeddings", False)

untied_model._tied_weights_keys = []
untied_model.lm_head.weight = torch.nn.Parameter(untied_model.lm_head.weight.clone())

print("tied weights:", find_tied_parameters(untied_model))

USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-untied-weights"

untied_model.push_to_hub(save_to)
tokenizer.push_to_hub(save_to)

# or save locally
save_to_local_path = f"{MODEL_NAME}-untied-weights"
untied_model.save_pretrained(save_to_local_path)
tokenizer.save_pretrained(save_to)

Note: to push_to_hub you need to run

Shell

pip install -U "huggingface_hub[cli]"
huggingface-cli login

and use a token with write access, from https://huggingface.co/settings/tokens

Quantization

We used following code to get the quantized model:

from transformers import (
  AutoModelForCausalLM,
  AutoProcessor,
  AutoTokenizer,
  TorchAoConfig,
)
from torchao.quantization.quant_api import (
    IntxWeightOnlyConfig,
    Int8DynamicActivationIntxWeightConfig,
    ModuleFqnToConfig,
    quantize_,
)
from torchao.quantization.granularity import PerGroup, PerAxis
import torch

# we start from the model with untied weights
model_id = "microsoft/Phi-4-mini-instruct"
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
untied_model_id = f"{USER_ID}/{MODEL_NAME}-untied-weights"
untied_model_local_path = f"{MODEL_NAME}-untied-weights"

embedding_config = IntxWeightOnlyConfig(
    weight_dtype=torch.int8,
    granularity=PerAxis(0),
)
linear_config = Int8DynamicActivationIntxWeightConfig(
    weight_dtype=torch.int4,
    weight_granularity=PerGroup(32),
    weight_scale_dtype=torch.bfloat16,
)
quant_config = ModuleFqnToConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
quantization_config = TorchAoConfig(quant_type=quant_config, include_input_output_embeddings=True, modules_to_not_convert=[])

# either use `untied_model_id` or `untied_model_local_path`
quantized_model = AutoModelForCausalLM.from_pretrained(untied_model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Push to hub
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-INT8-INT4"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)

# Manual testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
    {
        "role": "system",
        "content": "",
    },
    {"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
    templated_prompt,
    return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])

The response from the manual testing is:

Hello! As an AI, I don't have consciousness in the way humans do, but I am fully operational and here to assist you. How can I help you today?

Model Quality

We rely on lm-evaluation-harness to evaluate the quality of the quantized model.

Benchmark
	Phi-4-mini-ins	Phi-4-mini-instruct-INT8-INT4
Popular aggregated benchmark
mmlu (0 shot)	66.73	60.75
mmlu_pro (5-shot)	46.43	11.75
Reasoning
arc_challenge	56.91	48.46
gpqa_main_zeroshot	30.13	30.80
hellaswag	54.57	50.35
openbookqa	33.00	30.40
piqa (0-shot)	77.64	74.43
siqa	49.59	44.98
truthfulqa_mc2 (0-shot)	48.39	51.35
winogrande (0-shot)	71.11	70.32
Multilingual
mgsm_en_cot_en	60.80	57.60
Math
gsm8k (5-shot)	81.88	61.71
Mathqa (0-shot)	42.31	36.95
Overall	55.35	48.45

Reproduce Model Quality Results

Need to install lm-eval from source: https://github.com/EleutherAI/lm-evaluation-harness#install

baseline

lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8

int8 dynamic activation and int4 weight quantization (INT8-INT4)

lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-INT8-INT4 --tasks hellaswag --device cuda:0 --batch_size 8

Exporting to ExecuTorch

We can run the quantized model on a mobile phone using ExecuTorch. Once ExecuTorch is set-up, exporting and running the model on device is a breeze.

ExecuTorch's LLM export scripts require the checkpoint keys and parameters have certain names, which differ from those used in Hugging Face. So we first use a script that converts the Hugging Face checkpoint key names to ones that ExecuTorch expects:

python -m executorch.examples.models.phi_4_mini.convert_weights $(hf download pytorch/Phi-4-mini-instruct-INT8-INT4) pytorch_model_converted.bin

Once we have the checkpoint, we export it to ExecuTorch with a max_seq_length/max_context_length of 1024 to the XNNPACK backend as follows.

(Note: ExecuTorch LLM export script requires config.json have certain key names. The correct config to use for the LLM export script is located at examples/models/phi_4_mini/config/config.json within the ExecuTorch repo.)

Shell

python-m executorch.examples.models.llama.export_llama \
  --model "phi_4_mini" \
  --checkpoint pytorch_model_converted.bin \
  --params examples/models/phi_4_mini/config/config.json \
  --output_name model.pte \
  -kv \
  --use_sdpa_with_kv_cache \
  -X \
  --xnnpack-extended-ops \
  --max_context_length 1024 \
  --max_seq_length 1024 \
  --dtype fp32 \
  --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}'

After that you can run the model in a mobile app (see Running in a mobile app).

(We try to keep these instructions up-to-date, but if you find they do not work, check out our CI test in ExecuTorch for the latest source of truth, and let us know we need to update our model card.)

Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization

The model's quantization is powered by TorchAO, a framework presented in the paper TorchAO: PyTorch-Native Training-to-Serving Model Optimization.

Abstract: We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .

Resources

Official TorchAO GitHub Repository: https://github.com/pytorch/ao
TorchAO Documentation: https://docs.pytorch.org/ao/stable/index.html

Disclaimer

PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.

Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.