Instructions to use benhaotang/phi4-qwq-sky-t1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use benhaotang/phi4-qwq-sky-t1 with Transformers:

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

pipe = pipeline("text-generation", model="benhaotang/phi4-qwq-sky-t1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("benhaotang/phi4-qwq-sky-t1")
model = AutoModelForCausalLM.from_pretrained("benhaotang/phi4-qwq-sky-t1")
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 benhaotang/phi4-qwq-sky-t1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "benhaotang/phi4-qwq-sky-t1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "benhaotang/phi4-qwq-sky-t1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/benhaotang/phi4-qwq-sky-t1

SGLang

How to use benhaotang/phi4-qwq-sky-t1 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 "benhaotang/phi4-qwq-sky-t1" \
    --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": "benhaotang/phi4-qwq-sky-t1",
		"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 "benhaotang/phi4-qwq-sky-t1" \
        --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": "benhaotang/phi4-qwq-sky-t1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use benhaotang/phi4-qwq-sky-t1 with Docker Model Runner:
```
docker model run hf.co/benhaotang/phi4-qwq-sky-t1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using prithivMLmods/Phi-4-QwQ as a base.

Models Merged

The following models were included in the merge:

benhaotang/Phi-4-llama-t1-full but actually win10/Phi-4-llama-t1-lora, this is who and where you should really thank.
prithivMLmods/Phi-4-QwQ

Eval

IFEval is broken due to the Sky-T1 strict system prompt format, but other than that, seems to have recreated qwq at 14B.

Running

With Ollama

ollama run hf.co/benhaotang/phi4-qwq-sky-t1-Q4_K_M-GGUF

I suggest adding SYSTEM "You are a helpful AI asistent. You always think step by step." to triger step by step reasoning.

With pytorch

import transformers
tokenizer = AutoTokenizer.from_pretrained("mircosoft/phi-4")
pipeline = transformers.pipeline(
    "text-generation",
    model="benhaotang/phi4-qwq-sky-t1",
    tokenizer=tokenizer,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are a helpful AI asistent. You always think step by step."},
    {"role": "user", "content": "Give me a short intodcution to renormalization group(RG) flow in physcis?"},
]
outputs = pipeline(messages, max_new_tokens=128)
print(outputs[0]["generated_text"])

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: prithivMLmods/Phi-4-QwQ
    #no parameters necessary for base model
  - model: benhaotang/Phi-4-llama-t1-full
    parameters:
      density: 0.5
      weight: 0.5
  - model: prithivMLmods/Phi-4-QwQ
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: prithivMLmods/Phi-4-QwQ
parameters:
  normalize: false
  int8_mask: true
dtype: float16