pytorch/Phi-4-mini-instruct-float8dq

Phi4-mini model quantized with torchao float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team. Use it directly, or serve using vLLM with 36% VRAM reduction, 15-20% speedup and little to no accuracy impact on H100.

Inference with vLLM

Need to install vllm nightly to get some recent changes:

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

Code Example

from vllm import LLM, SamplingParams

llm = LLM(model="pytorch/Phi-4-mini-instruct-float8dq", trust_remote_code=True)

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?"},
]

sampling_params = SamplingParams(
  max_tokens=500,
  temperature=0.0,
)

output = llm.chat(messages=messages, sampling_params=sampling_params)
print(output[0].outputs[0].text)

Serving

Then we can serve with the following command:

vllm serve pytorch/Phi-4-mini-instruct-float8dq --tokenizer microsoft/Phi-4-mini-instruct -O3

Inference with Transformers

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
pip install torch
pip install accelerate

Example:

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:

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:

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):])

Model Quality

We rely on 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

float8 dynamic activation and float8 weight quantization (float8dq)

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	phi4-mini-float8dq
Popular aggregated benchmark
mmlu (0-shot)	66.73	Pending
mmlu_pro (5-shot)	46.43	Pending
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	Pending

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:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

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

Results (H100 machine)

Benchmark
	Phi-4 mini-Ins	phi4-mini-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.

benchmark_latency

Need to install vllm nightly to get some recent changes

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

Get vllm source code:

git clone [email protected]:vllm-project/vllm.git

Run the following under vllm root folder:

baseline

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

float8dq

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

benchmark_serving

We also benchmarked the throughput in a serving environment.

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

Get vllm source code:

git clone [email protected]:vllm-project/vllm.git

Run the following under vllm 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

float8dq

Server:

vllm serve pytorch/Phi-4-mini-instruct-float8dq --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 jerryzh168/phi4-mini-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.