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

Libraries

How to use FINGU-AI/Phi-4-RRStock with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINGU-AI/Phi-4-RRStock")
model = AutoModelForCausalLM.from_pretrained("FINGU-AI/Phi-4-RRStock")
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 FINGU-AI/Phi-4-RRStock with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FINGU-AI/Phi-4-RRStock

SGLang

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

Docker Model Runner
How to use FINGU-AI/Phi-4-RRStock with Docker Model Runner:
```
docker model run hf.co/FINGU-AI/Phi-4-RRStock
```

Phi-4 SLERP Merge Model

Model Description

This is a merged language model created using the Spherical Linear Interpolation (SLERP) merge method, allowing for a smooth blend of features from both parent models across different layers. The merge optimizes reasoning, general knowledge, and task-specific performance by strategically interpolating attention and MLP components.

Merge Details

Merge Method:
The model was merged using SLERP (Spherical Linear Interpolation) rather than a traditional linear merge, ensuring a well-balanced combination of both source models while maintaining coherent weight transitions.

Base Model:

bunnycore/Phi-4-RR-Shoup (used as the primary base)

Models Merged

The following models were included in this merge:

bunnycore/Phi-4-RR-Shoup (Primary base)
bunnycore/Phi-4-Model-Stock-v4

Configuration

The following YAML configuration was used to produce this merged model:

slices:
- sources:
  - model: bunnycore/Phi-4-RR-Shoup
    layer_range:
    - 0
    - 32
  - model: bunnycore/Phi-4-Model-Stock-v4
    layer_range:
    - 0
    - 32
merge_method: slerp
base_model: bunnycore/Phi-4-RR-Shoup
parameters:
  t:
  - filter: self_attn
    value:
    - 0
    - 0.5
    - 0.3
    - 0.7
    - 1
  - filter: mlp
    value:
    - 1
    - 0.5
    - 0.7
    - 0.3
    - 0
  - value: 0.5
dtype: bfloat16

Downloads last month: 6

Safetensors

Model size

12B params

Tensor type

F32

F16