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) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team. Use it directly, or serve using [vLLM](https://docs.vllm.ai/en/latest/) with 36% VRAM reduction, 15-20% speedup and little to no accuracy impact on H100.

# Inference with vLLM
Install vllm nightly to get some recent changes:
```
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```

## Code Example
```Py
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)


if __name__ == '__main__':
    # Create an LLM.
    llm = LLM(model="pytorch/Phi-4-mini-instruct-float8dq")
    # Generate texts from the prompts.
    # The output is a list of RequestOutput objects
    # that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    print("\nGenerated Outputs:\n" + "-" * 60)
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt:    {prompt!r}")
        print(f"Output:    {generated_text!r}")
        print("-" * 60)
```

Note: please use `VLLM_DISABLE_COMPILE_CACHE=1` to disable compile cache when running this code, e.g. `VLLM_DISABLE_COMPILE_CACHE=1 python example.py`, since there are some issues with the composability of compile in vLLM and torchao,
this is expected be resolved in pytorch 2.8.

## Serving
Then we can serve with the following command:
```Shell
vllm serve pytorch/Phi-4-mini-instruct-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3
```

# Inference with Transformers

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
pip install torch
pip install accelerate
```

Example:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
torch.random.manual_seed(0)

model_path = "pytorch/Phi-4-mini-instruct-float8dq"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
 
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
 
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
 
generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}
 
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```

# Quantization Recipe

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
pip install torch
pip install accelerate
```

Use the following code to get the quantized model:

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

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

from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow
quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
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}-float8dq"
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):])
```

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

# 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
```

## float8 dynamic activation and float8 weight quantization (float8dq)
```Shell
lm_eval --model hf --model_args pretrained=pytorch/Phi-4-mini-instruct-float8dq --tasks hellaswag --device cuda:0 --batch_size 8
```

| Benchmark                        |                |                               |
|----------------------------------|----------------|-------------------------------|
|                                  | Phi-4-mini-ins | Phi-4-mini-instruct-float8dq  | 
| **Popular aggregated benchmark** |                |                               |
| mmlu (0-shot)                    | 66.73          |  66.61                        |
| mmlu_pro (5-shot)                | 46.43          |  44.58                        |
| **Reasoning**                    |                |                               |
| arc_challenge (0-shot)           | 56.91          |  56.66                        |
| gpqa_main_zeroshot               | 30.13          |  29.46                        |
| HellaSwag                        | 54.57          |  54.55                        |
| openbookqa                       | 33.00          |  33.60                        |
| piqa (0-shot)	                   | 77.64          |  77.48                        |
| social_iqa                       | 49.59          |  49.28                        |
| truthfulqa_mc2 (0-shot)          | 48.39          |  48.09                        |
| winogrande  (0-shot)             | 71.11          |  72.77                        |
| **Multilingual**                 |                |                               |
| mgsm_en_cot_en                   | 60.8           |  60.0                         |
| **Math**                         |                |                               |
| gsm8k (5-shot)                   | 81.88          |  80.89                        |
| mathqa (0-shot)                  | 42.31          |  42.51                        |
| **Overall**                      | **55.35**       | **55.11**                    |

# Peak Memory Usage


## Results

| Benchmark        |                |                                |
|------------------|----------------|--------------------------------|
|                  | Phi-4 mini-Ins | Phi-4-mini-instruct-float8dq   | 
| Peak Memory (GB) | 8.91           | 5.70 (36% reduction)           |


## Benchmark Peak Memory

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


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

# use "microsoft/Phi-4-mini-instruct" or "pytorch/Phi-4-mini-instruct-float8dq"
model_id = "pytorch/Phi-4-mini-instruct-float8dq"
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

## Results (H100 machine)
| Benchmark                        |                |                               |
|----------------------------------|----------------|-------------------------------|
|                                  | Phi-4 mini-Ins | Phi-4-mini-instruct-float8dq  | 
| latency (batch_size=1)           | 1.64s          | 1.41s (16% speedup)           |
| latency (batch_size=128)         | 3.1s           | 2.72s (14% speedup)           |
| serving (num_prompts=1)          | 1.35 req/s     | 1.57 req/s (16% speedup)      |
| serving (num_prompts=1000)       | 66.68 req/s    | 80.53 req/s (21% speedup)     |

Note the result of latency (benchmark_latency) is in seconds, and serving (benchmark_serving) is in number of requests per second.

## Setup
Get vllm source code:
```Shell
git clone [email protected]:vllm-project/vllm.git
```

Install vllm
```
VLLM_USE_PRECOMPILED=1 pip install --editable .
```

Run the benchmarks under `vllm` root folder:

## benchmark_latency

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

### float8dq
```Shell
VLLM_DISABLE_COMPILE_CACHE=1 python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model pytorch/Phi-4-mini-instruct-float8dq --batch-size 1
```

## benchmark_serving

We benchmarked the throughput in a serving environment.

Download sharegpt dataset:

```Shell
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

Note: you can change the number of prompts to be benchmarked with `--num-prompts` argument for `benchmark_serving` script.
### baseline
Server:
```Shell
vllm serve microsoft/Phi-4-mini-instruct --tokenizer microsoft/Phi-4-mini-instruct -O3
```

Client:
```Shell
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
```

### float8dq
Server:
```Shell
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve pytorch/Phi-4-mini-instruct-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3
```

Client:
```Shell
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-float8dq --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.