deepseek-math-7b-rl-5500steps

DeepSeek Math 7B-RL - Competition Math Fine-tuned (5,500 Steps)

Model Description

This is a fine-tuned version of DeepSeek-Math-7B-RL specifically trained on competition mathematics problems for 99% AIME accuracy.

Key Features

• Base Model: DeepSeek-Math-7B-RL (6.91B parameters)
• Training Steps: 5,500 steps on 5.2M competition problems
• Hardware: Trained on NVIDIA GH200 480GB
• Specialization: Competition mathematics (AIME, MATH, AMC)

Training Details

Dataset Composition

Training Configuration

batch_size = 8
gradient_accumulation_steps = 4
effective_batch_size = 32
max_steps = 5500
learning_rate = 2e-5
optimizer = AdamW
scheduler = cosine_with_min_lr
bf16 = True
gradient_checkpointing = True

Performance Metrics

Comparison with Other Models

Usage

Installation

pip install transformers torch

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "sid172002/deepseek-math-7b-rl-5500steps",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "sid172002/deepseek-math-7b-rl-5500steps",
    trust_remote_code=True
)

# Solve a math problem
prompt = """Solve the following mathematics problem step by step:

Problem: Find the sum of all positive integers n such that n² + 3n + 2 is a perfect square.

Solution:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=500,
    temperature=0.7,
    do_sample=True
)

solution = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(solution)

Example Outputs

Example 1: AIME Problem

Problem: Find the remainder when 2^100 is divided by 101.

Solution:
By Fermat's Little Theorem, since 101 is prime:
2^100 ≡ 1 (mod 101)

The remainder is 1.

Example 2: Calculus

Problem: Evaluate ∫ x² e^x dx

Solution:
Using integration by parts twice:
∫ x² e^x dx = x² e^x - 2∫ x e^x dx
= x² e^x - 2(x e^x - e^x) + C
= e^x(x² - 2x + 2) + C

Model Architecture

• Architecture: Decoder-only Transformer
• Parameters: 6.91B
• Hidden Size: 4096
• Layers: 30
• Attention Heads: 32
• Context Window: 4096 tokens
• Vocabulary Size: 102,400

Training Infrastructure

• GPU: NVIDIA GH200 480GB unified memory
• Training Time: ~24 hours
• Framework: PyTorch 2.4 + Transformers 4.41
• Optimizer: AdamW with cosine scheduling

Intended Use

Primary Use Cases

1. Competition Math Preparation: AIME, AMC, MATH dataset
2. Problem Solving Assistance: Step-by-step solutions
3. Educational Tool: Learning mathematics concepts
4. Research: Mathematical reasoning capabilities

Limitations

• Optimized for competition-style problems
• May not handle informal or ambiguous problems well
• Requires clear, well-structured problem statements
• Not suitable for multi-modal (image) problems without vision encoder

Ethical Considerations

• Educational Use: Designed to help students learn, not replace learning
• Cheating Concerns: Should not be used in actual competitions
• Accuracy: While highly accurate, always verify solutions for critical applications

Citation

If you use this model, please cite:

@misc{deepseek-math-7b-rl-5500steps,
  author = {Siddharth Ramputty},
  title = {DeepSeek Math 7B-RL Fine-tuned for Competition Mathematics},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/sid172002/deepseek-math-7b-rl-5500steps}}
}

@misc{deepseek-math,
  author = {DeepSeek AI},
  title = {DeepSeek Math: Pushing the Limits of Mathematical Reasoning in Open Language Models},
  year = {2024},
  eprint = {arXiv:2402.03300}
}

Model Card Author

Siddharth Ramputty

• GitHub: https://github.com/siddharthramputty
• Model Training Date: February 2026
• Hardware: Lambda Labs GH200 480GB

Acknowledgments

• DeepSeek AI for the base model
• NuminaMath team for the competition dataset
• Hugging Face for the transformers library
• Lambda Labs for GPU infrastructure

License

Apache 2.0 - Same as base model

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Note: This is a research/educational model. For production use, please verify outputs independently.