Instructions to use gulf-inference/phi-4-mini-enhanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gulf-inference/phi-4-mini-enhanced with Transformers:

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

pipe = pipeline("text-generation", model="gulf-inference/phi-4-mini-enhanced", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gulf-inference/phi-4-mini-enhanced", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("gulf-inference/phi-4-mini-enhanced", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gulf-inference/phi-4-mini-enhanced with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gulf-inference/phi-4-mini-enhanced"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gulf-inference/phi-4-mini-enhanced",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gulf-inference/phi-4-mini-enhanced

SGLang

How to use gulf-inference/phi-4-mini-enhanced 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 "gulf-inference/phi-4-mini-enhanced" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gulf-inference/phi-4-mini-enhanced",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "gulf-inference/phi-4-mini-enhanced" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gulf-inference/phi-4-mini-enhanced",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gulf-inference/phi-4-mini-enhanced with Docker Model Runner:
```
docker model run hf.co/gulf-inference/phi-4-mini-enhanced
```

phi-4-mini-enhanced / README.md

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metadata

tags:
  - phi
  - phi-4
  - microsoft
  - causal-lm
  - text-generation
  - enhanced
  - optimized
  - lightweight
  - efficient
  - transformers
license: mit
library_name: transformers
pipeline_tag: text-generation
language:
  - en
  - multilingual

Phi-4 Mini Enhanced

Enhanced version of Microsoft Phi-4-mini-instruct with optimized inference and extended context support.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "gulf-inference/phi-4-mini-enhanced",
    trust_remote_code=True,
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("gulf-inference/phi-4-mini-enhanced")

Key Improvements

Faster inference: optimized attention
Extended context: up to 8K tokens
Drop-in replacement — same API, better performance

License

MIT