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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drawais/Phi-4-mini-instruct-AWQ-INT4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("drawais/Phi-4-mini-instruct-AWQ-INT4", trust_remote_code=True)
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drawais/Phi-4-mini-instruct-AWQ-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": "drawais/Phi-4-mini-instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use drawais/Phi-4-mini-instruct-AWQ-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 "drawais/Phi-4-mini-instruct-AWQ-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": "drawais/Phi-4-mini-instruct-AWQ-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 "drawais/Phi-4-mini-instruct-AWQ-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": "drawais/Phi-4-mini-instruct-AWQ-INT4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Phi-4-mini-instruct-AWQ-INT4 / README.md

drawais

Initial upload of Phi-4-mini-instruct-AWQ-INT4

572b359 verified 5 days ago

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metadata

license: mit
license_link: https://opensource.org/license/mit
base_model: microsoft/Phi-4-mini-instruct
tags:
  - quantized
  - 4-bit
  - int4
  - awq
language:
  - en
library_name: transformers
pipeline_tag: text-generation

Phi-4-mini-instruct-AWQ-INT4

INT4 weight-only quantization of microsoft/Phi-4-mini-instruct.

Microsoft Phi-4-mini-instruct in INT4. About 2.5 GB on disk. Runs on a 4 GB consumer GPU.

Property	Value
Base model	microsoft/Phi-4-mini-instruct
Quantization	INT4 weight-only
Approx. on-disk size	~2.9 GB
License	MIT License
Languages	English

Load (vLLM)

vllm serve drawais/Phi-4-mini-instruct-AWQ-INT4 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.94

from vllm import LLM, SamplingParams
llm = LLM(model="drawais/Phi-4-mini-instruct-AWQ-INT4", max_model_len=32768)
print(llm.generate(["Hello!"], SamplingParams(max_tokens=128))[0].outputs[0].text)

Footprint

~2.9 GB on disk. Recommended VRAM: enough headroom for KV cache.

License & attribution

This artifact is a derivative work of microsoft/Phi-4-mini-instruct, released by its original authors under the MIT License.

This artifact is distributed under the same license. The full license text is included in LICENSE, and required attribution is in NOTICE.

License text: https://opensource.org/license/mit Source model: https://huggingface.co/microsoft/Phi-4-mini-instruct