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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Phi-4-Stock-RP")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Phi-4-Stock-RP")
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 Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

Phi-4-Stock-RP is a phi4 based language model designed for reasoning and role-play scenarios. It leverages the capabilities of several pre-existing high-quality models, integrating them into a cohesive system that excels in reasoning, creative, narrative, and interactive text generation.

Training Data:

Sources: Merged from various pre-trained models, focusing on those with strong performance in text generation and understanding. Enhanced with a specialized LoRA trained on role-play dialogues, scenarios, and character interactions. Model Capabilities:
Role-Playing: Capable of maintaining coherent characters, plots, and dialogues over extended interactions. Creative Writing: Assists in crafting stories, dialogues, and character development with a focus on immersion and narrative coherence. General Language Understanding: Inherits general text comprehension and generation from the base models, making it versatile for various language tasks beyond RP.

<|im_start|>system<|im_sep|> {system_message}<|im_end|> <|im_start|>user<|im_sep|> {prompt}<|im_end|> <|im_start|>assistant<|im_sep|>

Merge Method

This model was merged using the passthrough merge method using bunnycore/Phi-4-Model-Stock + bunnycore/Phi-4-rp-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+bunnycore/Phi-4-rp-v1-lora
dtype: bfloat16
merge_method: passthrough
models:
  - model: bunnycore/Phi-4-Model-Stock+bunnycore/Phi-4-rp-v1-lora
tokenizer_source: unsloth/phi-4