naazimsnh02/ernie-45-math-finetuned

ERNIE-4.5 Fine-tuned for Mathematical Reasoning

This model is a fine-tuned version of unsloth/ERNIE-4.5-21B-A3B-PT on the nvidia/Nemotron-RL-math-OpenMathReasoning dataset.

Model Description

This model specializes in solving complex mathematical problems including:

• —Algebra (equations, factoring, systems)
• —Calculus (derivatives, integrals)
• —Geometry and trigonometry
• —Word problems requiring multi-step reasoning
• —Competition-level mathematics

Training Details

Training Data

• —Dataset: nvidia/Nemotron-RL-math-OpenMathReasoning
• —Training Samples: 7,600
• —Evaluation Samples: 400
• —Format: Conversational (ERNIE-4.5 format)

Training Configuration

• —Base Model: unsloth/ERNIE-4.5-21B-A3B-PT (21B parameters)
• —Method: QLoRA (4-bit quantization + LoRA)
• —LoRA Rank: 16
• —LoRA Alpha: 16
• —Trainable Parameters: 355,090,432 (3.11% of total)

Hyperparameters

• —Batch Size: 4 (per device)
• —Gradient Accumulation: 2
• —Effective Batch Size: 8
• —Learning Rate: 0.0002
• —LR Scheduler: Cosine with warmup
• —Warmup Ratio: 0.05
• —Training Steps: 707 (stopped early for optimal performance)
• —Optimizer: AdamW 8-bit
• —Precision: BF16

Training Results

• —Final Training Loss: 0.6046
• —Final Validation Loss: 0.6115
• —Best Validation Loss: 0.6115
• —Loss Improvement: 9.2% (from 0.6732 to 0.6115)
• —Training Time: 4.64 hours
• —GPU: NVIDIA A100-SXM4-40GB
• —Peak Memory: 19.375 GB / 39.494 GB (49.058%)

Framework

• —Unsloth: 2x faster training, 70% less memory
• —Modal: Serverless GPU infrastructure (40GB A100)
• —Transformers: 4.56.2
• —TRL: 0.22.2

Usage

from unsloth import FastModel

# Load the fine-tuned model
model, tokenizer = FastModel.from_pretrained(
    model_name="naazimsnh02/ernie-45-math-finetuned",
    max_seq_length=2048,
    load_in_4bit=True,
    full_finetuning=False,
)

# Prepare for inference
FastModel.for_inference(model)

# Solve a math problem
messages = [{
    "role": "user",
    "content": "Solve the equation: 2x² + 5x - 3 = 0"
}]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", padding=True).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
)

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

Example Output

Input:

Solve the equation: x² + 5x + 6 = 0

Output:

To solve x² + 5x + 6 = 0, we can factor:

Find two numbers that multiply to 6 and add to 5:
2 and 3 work because 2 × 3 = 6 and 2 + 3 = 5

Factored form:
(x + 2)(x + 3) = 0

Setting each factor to zero:
x + 2 = 0  →  x = -2
x + 3 = 0  →  x = -3

Therefore: \boxed{x = -2, -3}

Training Progress

Training stopped at step 700 for optimal validation loss.

Training Infrastructure

• —Platform: Modal (modal.com)
• —GPU: 40GB A100
• —Training Duration: ~4.6 hours
• —Checkpointing: Every 100 steps
• —Evaluation: Every 100 steps

Limitations

• —Optimized for mathematical reasoning; may not perform as well on other domains
• —Trained on English language problems only
• —Best results with problems similar to training data format
• —Requires GPU for inference (4-bit quantization)

Citation

@misc{ernie45-math-2025,
  title={ERNIE-4.5 Fine-tuned for Mathematical Reasoning},
  author={naazimsnh02},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/naazimsnh02/ernie-45-math-finetuned}}
}

Acknowledgments

• —ERNIE Team for the base model
• —Unsloth for optimization framework
• —NVIDIA for the Nemotron-RL dataset
• —Modal for GPU infrastructure
• —ERNIE AI Developer Challenge for the opportunity

License

MIT License - See repository for details

Trained with ❤️ using Unsloth and Modal