Instructions to use pytorch/Phi-4-mini-instruct-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pytorch/Phi-4-mini-instruct-INT4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pytorch/Phi-4-mini-instruct-INT4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pytorch/Phi-4-mini-instruct-INT4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pytorch/Phi-4-mini-instruct-INT4", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pytorch/Phi-4-mini-instruct-INT4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pytorch/Phi-4-mini-instruct-INT4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pytorch/Phi-4-mini-instruct-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pytorch/Phi-4-mini-instruct-INT4

SGLang

How to use pytorch/Phi-4-mini-instruct-INT4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pytorch/Phi-4-mini-instruct-INT4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pytorch/Phi-4-mini-instruct-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pytorch/Phi-4-mini-instruct-INT4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pytorch/Phi-4-mini-instruct-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pytorch/Phi-4-mini-instruct-INT4 with Docker Model Runner:
```
docker model run hf.co/pytorch/Phi-4-mini-instruct-INT4
```

jerryzh168 commited on Apr 19, 2025

Commit

3737ba9

verified ·

1 Parent(s): 0e7b2e9

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -11

README.md CHANGED Viewed

@@ -65,17 +65,17 @@ print(f"{save_to} model:", benchmark_fn(quantized_model.generate, **inputs, max_
 # Model Quality
 We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
-# Installing the nightly version to get most recent updates
 ```
 pip install git+https://github.com/EleutherAI/lm-evaluation-harness
 ```
-# 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=jerryzh168/phi4-mini-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
 ```
@@ -98,38 +98,38 @@ lm_eval --model hf --model_args pretrained=jerryzh168/phi4-mini-int4wo-hqq --tas
 Our int4wo is only optimized for batch size 1, so we'll only benchmark the batch size 1 performance with vllm.
 For batch size N, please see our [gemlite checkpoint](https://huggingface.co/jerryzh168/phi4-mini-int4wo-gemlite).
-# Download vllm source code and install vllm
 ```
 git clone git@github.com:vllm-project/vllm.git
 VLLM_USE_PRECOMPILED=1 pip install .
 ```
-# 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
 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 jerryzh168/phi4-mini-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
@@ -140,7 +140,7 @@ 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 jerryzh168/phi4-mini-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.
+## Installing the nightly version to get most recent updates
 ```
 pip install git+https://github.com/EleutherAI/lm-evaluation-harness
 ```
+## 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=jerryzh168/phi4-mini-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
 ```
 Our int4wo is only optimized for batch size 1, so we'll only benchmark the batch size 1 performance with vllm.
 For batch size N, please see our [gemlite checkpoint](https://huggingface.co/jerryzh168/phi4-mini-int4wo-gemlite).
+## Download vllm source code and install vllm
 ```
 git clone git@github.com:vllm-project/vllm.git
 VLLM_USE_PRECOMPILED=1 pip install .
 ```
+## 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
 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 jerryzh168/phi4-mini-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
 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 jerryzh168/phi4-mini-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3