---
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) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, by PyTorch team.

# Quantization Recipe

First need to install the required packages:
```
pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
```

We used following code to get the quantized model:
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

model_id = "microsoft/Phi-4-mini-instruct"

from torchao.quantization import Int4WeightOnlyConfig
quant_config = Int4WeightOnlyConfig(group_size=128, use_hqq=True)
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Push to hub
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-int4wo-hqq"
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):])

# Local Benchmark
import torch.utils.benchmark as benchmark
from torchao.utils import benchmark_model
import torchao

def benchmark_fn(f, *args, **kwargs):
    # Manual warmup
    for _ in range(2):
        f(*args, **kwargs)

    t0 = benchmark.Timer(
        stmt="f(*args, **kwargs)",
        globals={"args": args, "kwargs": kwargs, "f": f},
        num_threads=torch.get_num_threads(),
    )
    return f"{(t0.blocked_autorange().mean):.3f}"

torchao.quantization.utils.recommended_inductor_config_setter()
quantized_model = torch.compile(quantized_model, mode="max-autotune")
print(f"{save_to} model:", benchmark_fn(quantized_model.generate, **inputs, max_new_tokens=128))
```

# Serving with vllm
We can use the same command we used in serving benchmarks to serve the model with vllm
```
vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
```

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

## int4wo-hqq
```
lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
```

| Benchmark                        |                |                     |
|----------------------------------|----------------|---------------------|
|                                  | Phi-4 mini-Ins | phi4-mini-int4wo    | 
| **Popular aggregated benchmark** |                |                     |
| mmlu (0-shot)                    | 66.73          |  63.56              |
| mmlu_pro (5-shot)                | 46.43          |  36.74              |
| **Reasoning**                    |                |                     |
| arc_challenge (0-shot)           | 56.91          |  54.86              |
| gpqa_main_zeroshot               | 30.13          |  30.58              |
| HellaSwag                        | 54.57          |  53.54              |
| openbookqa                       | 33.00          |  34.40              |
| piqa (0-shot)	                   | 77.64          |  76.33              |
| social_iqa                       | 49.59          |  47.90              |
| truthfulqa_mc2 (0-shot)          | 48.39          |  46.44              |
| winogrande  (0-shot)             | 71.11          |  71.51              |
| **Multilingual**                 |                |                     |
| mgsm_en_cot_en                   | 60.8           |  59.6               |
| **Math**                         |                |                     |
| gsm8k (5-shot)                   | 81.88          |  74.37              |
| mathqa (0-shot)                  | 42.31          |  42.75              |
| **Overall**                      | **TODO**       | **TODO**            |
 

# Peak Memory Usage

We can use the following code to get a sense of peak memory usage during inference:

## Results

| Benchmark        |                |                                |
|------------------|----------------|--------------------------------|
|                  | Phi-4 mini-Ins | Phi-4-mini-instruct-int4wo-hqq | 
| Peak Memory (GB) | 8.91           | 2.98 (67% reduction)           |


## Benchmark Peak Memory

```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

# use "microsoft/Phi-4-mini-instruct" or "pytorch/Phi-4-mini-instruct-int4wo-hqq"
model_id = "microsoft/Phi-4-mini-instruct"
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)

torch.cuda.reset_peak_memory_stats()

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):])

mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Peak Memory Usage: {mem:.02f} GB")
```

# Model Performance

Our int4wo is only optimized for batch size 1, so we'll see slowdown in larger batch sizes, we expect this to be used in local server deployment for single or a few users
and decode tokens per second will be more important than time to first token.


## Results (A100 machine)
| Benchmark (Latency)              |                |                          |
|----------------------------------|----------------|--------------------------|
|                                  | Phi-4 mini-Ins | phi4-mini-int4wo-hqq     | 
| latency (batch_size=1)           | 2.46s          | 2.2s (12% speedup)       |
| serving (num_prompts=1)          | 0.87 req/s     | 1.05 req/s (20% speedup) |

Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second.
Int4 weight only is optimized for batch size 1 and short input and output token length, please stay tuned for models optimized for larger batch sizes or longer token length.

## Download dataset
Download sharegpt dataset: `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`

Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
## benchmark_latency

Need to install vllm nightly to get some recent changes
```
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```


Run the following under `vllm` source code root folder:

### baseline
```
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model microsoft/Phi-4-mini-instruct --batch-size 1
```

### int4wo-hqq
```
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model pytorch/Phi-4-mini-instruct-int4wo-hqq --batch-size 1
```

## benchmark_serving

We also benchmarked the throughput in a serving environment.


Run the following under `vllm` source code root folder:

### baseline
Server:
```
vllm serve microsoft/Phi-4-mini-instruct --tokenizer microsoft/Phi-4-mini-instruct -O3
```

Client:
```
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model microsoft/Phi-4-mini-instruct --num-prompts 1
```

### int4wo-hqq
Server:
```
vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3 --pt-load-map-location cuda:0
```

Client:
```
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model pytorch/Phi-4-mini-instruct-int4wo-hqq --num-prompts 1
```


# 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.