Instructions to use bunnycore/Phi-4-RP-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Phi-4-RP-v0 with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Phi-4-RP-v0", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Phi-4-RP-v0", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("bunnycore/Phi-4-RP-v0", trust_remote_code=True, device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bunnycore/Phi-4-RP-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunnycore/Phi-4-RP-v0"
# 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-RP-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bunnycore/Phi-4-RP-v0

SGLang

How to use bunnycore/Phi-4-RP-v0 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-RP-v0" \
    --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-RP-v0",
		"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-RP-v0" \
        --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-RP-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bunnycore/Phi-4-RP-v0 with Docker Model Runner:
```
docker model run hf.co/bunnycore/Phi-4-RP-v0
```
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 linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: bunnycore/Phi-4-RP
    parameters:
      weight: 1.0
  - model: Undi95/Phi4-abliterated
    parameters:
      weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.32
IFEval (0-Shot)	68.27
BBH (3-Shot)	54.84
MATH Lvl 5 (4-Shot)	27.79
GPQA (0-shot)	13.65
MuSR (0-shot)	10.86
MMLU-PRO (5-shot)	48.49

Downloads last month: 68

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for bunnycore/Phi-4-RP-v0

Base model

Undi95/Phi4-abliterated

Finetuned

(1)

this model

Quantizations

2 models

Spaces using bunnycore/Phi-4-RP-v0 15

Paper for bunnycore/Phi-4-RP-v0

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 10

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

68.270
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

54.840
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

27.790
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

13.650
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

10.860
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

48.490