README.md

---
library_name: transformers
tags:
- torchao
- phi
- phi4
- nlp
- code
- math
- chat
- conversational
license: mit
language:
- multilingual
base_model:
- microsoft/Phi-4-mini-instruct
pipeline_tag: text-generation
---

[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) is quantized by the PyTorch team using [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) with 8-bit embeddings and 8-bit dynamic activations with 4-bit weight linears (8da4w).
The model is suitable for mobile deployment with [ExecuTorch](https://github.com/pytorch/executorch).

We provide the [quantized pte](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) for direct use in ExecuTorch.
(The provided pte file is exported with the default max_seq_length/max_context_length of 128; if you wish to change this, re-export the quantized model following the instructions in [Exporting to ExecuTorch](#exporting-to-executorch).)  

# Running in a mobile app
The [pte file](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w.pte) can be run with ExecuTorch on a mobile phone.  See the [instructions](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) for doing this in iOS.
On iPhone 15 Pro, the model runs at 17.3 tokens/sec and uses 3206 Mb of memory.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66049fc71116cebd1d3bdcf4/521rXwIlYS9HIAEBAPJjw.png)

# Quantization Recipe

First need to install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao --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:

```Py
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)
```

## Quantization

We used following code to get the quantized model:

```Py
from transformers import (
  AutoModelForCausalLM,
  AutoProcessor,
  AutoTokenizer,
  TorchAoConfig,
)
from torchao.quantization.quant_api import (
    IntxWeightOnlyConfig,
    Int8DynamicActivationIntxWeightConfig,
    AOPerModuleConfig,
    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"

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 = AOPerModuleConfig({"_default": linear_config, "model.embed_tokens": embedding_config})
quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True, untie_embedding_weights=True, modules_to_not_convert=[])

quantized_model = AutoModelForCausalLM.from_pretrained(untied_model_id, torch_dtype=torch.float32, device_map="auto", 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}-untied-8da4w"
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](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.

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

## baseline
```Shell
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 (8da4w)
```Shell
lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-8da4w --tasks hellaswag --device cuda:0 --batch_size 8
```

| Benchmark                        |             |                   |
|----------------------------------|-------------|-------------------|
|                                  | Phi-4 mini-Ins | phi4-mini-8da4w| 
| **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             |


# Exporting to ExecuTorch

We can run the quantized model on a mobile phone using [ExecuTorch](https://github.com/pytorch/executorch).
Once ExecuTorch is [set-up](https://pytorch.org/executorch/main/getting-started.html), exporting and running the model on device is a breeze.

We first convert the [quantized checkpoint](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/pytorch_model.bin) to one ExecuTorch's LLM export script expects by renaming some of the checkpoint keys.
The following script does this for you.  We have uploaded the converted checkpoint [phi4-mini-8da4w-converted.bin](https://huggingface.co/pytorch/Phi-4-mini-instruct-8da4w/blob/main/phi4-mini-8da4w-converted.bin) for convenience.
```Shell
python -m executorch.examples.models.phi_4_mini.convert_weights pytorch_model.bin phi4-mini-8da4w-converted.bin
```

Once the checkpoint is converted, we can export to ExecuTorch's pte format with the XNNPACK delegate.
The below command exports with a max_seq_length/max_context_length of 128, which is the default value.

```Shell
PARAMS="executorch/examples/models/phi_4_mini/config.json"
python -m executorch.examples.models.llama.export_llama \
  --model "phi_4_mini" \
  --checkpoint "phi4-mini-8da4w-converted.bin" \
  --params "$PARAMS" \
  -kv \
  --use_sdpa_with_kv_cache \
  -X \
  --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}' \
  --max_seq_length 128 \
  --max_context_length 128 \
  --output_name="phi4-mini-8da4w.pte"
```

After that you can run the model in a mobile app (see [Running in a mobile app](#running-in-a-mobile-app) at the start of the README).

# 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.