Instructions to use bunnycore/Phi-4-ReasoningRP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Phi-4-ReasoningRP with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Phi-4-ReasoningRP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Phi-4-ReasoningRP")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Phi-4-ReasoningRP")
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 bunnycore/Phi-4-ReasoningRP with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bunnycore/Phi-4-ReasoningRP

SGLang

How to use bunnycore/Phi-4-ReasoningRP 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 "bunnycore/Phi-4-ReasoningRP" \
    --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": "bunnycore/Phi-4-ReasoningRP",
		"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 "bunnycore/Phi-4-ReasoningRP" \
        --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": "bunnycore/Phi-4-ReasoningRP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bunnycore/Phi-4-ReasoningRP with Docker Model Runner:
```
docker model run hf.co/bunnycore/Phi-4-ReasoningRP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is Phi-4 with a reasoning fine-tuned LoRA applied. While it can follow a reasoning format, it's important to understand that its "thinking" isn't the same as more advanced reasoning models (like R1 or O1). Think of it as Phi-4 with a helpful reasoning boost.

What can it do?

This model is designed for roleplay and other reasoning-related tasks. It's not intended to be a replacement for specialized reasoning models; it has its own strengths and limitations.

To activate the reasoning format, use the tag within the system prompt. This will encourage the model to structure its response in a step-by-step or explanatory manner.

Chat Template:

<|im_start|>system<|im_sep|>{system_prompt}<|im_end|>
<|im_start|>user<|im_sep|>{user}<|im_end|>
<|im_start|>assistant<|im_sep|>

Example System Prompt (with reasoning):

You are a helpful assistant. <think> Let's break this down step by step. First, we need to consider... Then, we can look at... Finally, we arrive at the answer. </think> Strengths:

Capable of roleplay.
Can follow a reasoning format when prompted.
Based on the Phi-4 architecture.

Benchmark:

Merge Details

Merge Method

This model was merged using the Passthrough merge method using bunnycore/Phi-4-Model-Stock-v4 + bunnycore/Phi-4-14B-1M-RRP-v1-lora as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: bunnycore/Phi-4-Model-Stock-v4+bunnycore/Phi-4-14B-1M-RRP-v1-lora
dtype: bfloat16
merge_method: passthrough
models:
  - model: bunnycore/Phi-4-Model-Stock-v4+bunnycore/Phi-4-14B-1M-RRP-v1-lora