Instructions to use pragnyanramtha/phi-4-math-rplus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pragnyanramtha/phi-4-math-rplus with Transformers:

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

pipe = pipeline("text-generation", model="pragnyanramtha/phi-4-math-rplus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pragnyanramtha/phi-4-math-rplus")
model = AutoModelForCausalLM.from_pretrained("pragnyanramtha/phi-4-math-rplus")
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 pragnyanramtha/phi-4-math-rplus with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pragnyanramtha/phi-4-math-rplus

SGLang

How to use pragnyanramtha/phi-4-math-rplus 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 "pragnyanramtha/phi-4-math-rplus" \
    --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": "pragnyanramtha/phi-4-math-rplus",
		"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 "pragnyanramtha/phi-4-math-rplus" \
        --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": "pragnyanramtha/phi-4-math-rplus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use pragnyanramtha/phi-4-math-rplus 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 pragnyanramtha/phi-4-math-rplus 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 pragnyanramtha/phi-4-math-rplus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pragnyanramtha/phi-4-math-rplus to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="pragnyanramtha/phi-4-math-rplus",
    max_seq_length=2048,
)

Docker Model Runner
How to use pragnyanramtha/phi-4-math-rplus with Docker Model Runner:
```
docker model run hf.co/pragnyanramtha/phi-4-math-rplus
```

Phi-4 Reasoning Plus - Math SFT

This model is a Supervised finetuned version of microsoft/Phi-4-reasoning-plus for mathematical reasoning tasks with 30k problems from aime , numina math dataset , and various other problems.

Training Details

Base Model: microsoft/Phi-4-reasoning-plus
Fine-tuning Method: LoRA (Low-Rank Adaptation) with Unsloth
LoRA Config: r=512, alpha=512
Target Modules: lm_head, o_proj, v_proj, up_proj, down_proj, k_proj, q_proj, gate_proj, embed_tokens
Precision: bfloat16

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "pragnyanramtha/phi-4-math-rplus",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("pragnyanramtha/phi-4-math-rplus")

# For math problems
messages = [
    {"role": "system", "content": "You are a helpful math assistant. Solve problems step by step."},
    {"role": "user", "content": "What is the sum of the first 100 positive integers?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: -

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for pragnyanramtha/phi-4-math-rplus

Base model

microsoft/phi-4

Finetuned

microsoft/Phi-4-reasoning-plus

Adapter

(5)

this model