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))