Instructions to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tensorblock/unsloth_Phi-4-reasoning-plus-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/unsloth_Phi-4-reasoning-plus-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/unsloth_Phi-4-reasoning-plus-GGUF",
	filename="Phi-4-reasoning-plus-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

SGLang

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF 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 "tensorblock/unsloth_Phi-4-reasoning-plus-GGUF" \
    --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": "tensorblock/unsloth_Phi-4-reasoning-plus-GGUF",
		"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 "tensorblock/unsloth_Phi-4-reasoning-plus-GGUF" \
        --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": "tensorblock/unsloth_Phi-4-reasoning-plus-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with Ollama:
```
ollama run hf.co/tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/unsloth_Phi-4-reasoning-plus-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tensorblock/unsloth_Phi-4-reasoning-plus-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/unsloth_Phi-4-reasoning-plus-GGUF to start chatting

Docker Model Runner
How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K
```

Lemonade

How to use tensorblock/unsloth_Phi-4-reasoning-plus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/unsloth_Phi-4-reasoning-plus-GGUF:Q2_K

Run and chat with the model

lemonade run user.unsloth_Phi-4-reasoning-plus-GGUF-Q2_K

List all available models

lemonade list

unsloth/Phi-4-reasoning-plus - GGUF

Join our Discord to learn more about what we're building ↗

This repo contains GGUF format model files for unsloth/Phi-4-reasoning-plus.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

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Prompt template

<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|><|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|><|im_start|>assistant<|im_sep|>

Model file specification

Filename	Quant type	File Size	Description
Phi-4-reasoning-plus-Q2_K.gguf	Q2_K	5.547 GB	smallest, significant quality loss - not recommended for most purposes
Phi-4-reasoning-plus-Q3_K_S.gguf	Q3_K_S	6.505 GB	very small, high quality loss
Phi-4-reasoning-plus-Q3_K_M.gguf	Q3_K_M	7.363 GB	very small, high quality loss
Phi-4-reasoning-plus-Q3_K_L.gguf	Q3_K_L	7.930 GB	small, substantial quality loss
Phi-4-reasoning-plus-Q4_0.gguf	Q4_0	8.383 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Phi-4-reasoning-plus-Q4_K_S.gguf	Q4_K_S	8.441 GB	small, greater quality loss
Phi-4-reasoning-plus-Q4_K_M.gguf	Q4_K_M	9.053 GB	medium, balanced quality - recommended
Phi-4-reasoning-plus-Q5_0.gguf	Q5_0	10.152 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Phi-4-reasoning-plus-Q5_K_S.gguf	Q5_K_S	10.152 GB	large, low quality loss - recommended
Phi-4-reasoning-plus-Q5_K_M.gguf	Q5_K_M	10.604 GB	large, very low quality loss - recommended
Phi-4-reasoning-plus-Q6_K.gguf	Q6_K	12.030 GB	very large, extremely low quality loss
Phi-4-reasoning-plus-Q8_0.gguf	Q8_0	15.581 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/unsloth_Phi-4-reasoning-plus-GGUF --include "Phi-4-reasoning-plus-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/unsloth_Phi-4-reasoning-plus-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 27

GGUF

Model size

15B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

Model tree for tensorblock/unsloth_Phi-4-reasoning-plus-GGUF

Base model

microsoft/phi-4

Finetuned

microsoft/Phi-4-reasoning-plus

Finetuned

unsloth/Phi-4-reasoning-plus

Quantized

(2)

this model