rimon-dutta/Rimon-Math-3B-V1

Rimon-Math-3B-V1

Rimon-Math-3B-V1 is a specialized 3-billion-parameter causal language model, fine-tuned for high-accuracy mathematical reasoning and logical problem-solving. Built on the Llama-3.2-3B-Instruct architecture and optimized using the Unsloth framework, this model excels at generating structured, step-by-step solutions (Chain-of-Thought).

Highlights

• —Reasoning Focused: Trained specifically to break down complex problems into logical steps.
• —Lightweight & Efficient: Optimized for consumer-grade GPUs (T4, RTX 3060+) and edge deployment.
• —High Compatibility: Works seamlessly with transformers, vLLM, and supports GGUF conversion for local use.

Model Capabilities

The model is fine-tuned to handle various mathematical domains:

• —Algebra: Solving equations, inequalities, and system of equations.
• —Calculus: Derivatives, integrals, and limit problems.
• —Geometry & Trigonometry: Properties of shapes and trigonometric identities.
• —Logic & Arithmetic: Multi-step word problems and sequence analysis.

Training Metrics (Approximation)

Usage Guide

Installation & Dependencies

To run Rimon-Math-3B-V1 efficiently, ensure you have the latest versions of the following libraries installed. Run this command in your terminal or a notebook cell:

pip install -U transformers torch accelerate bitsandbytes sentencepiece

How to Use the Model

You can load the model in two different modes depending on your hardware resources.

Option 1: 4-bit Quantization (Low VRAM Mode)

Best for users on Google Colab (Free T4) or laptops with limited GPU memory. This uses only ~3.5 GB of VRAM.

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "rimon-dutta/Rimon-Math-3B-V1"

# 4-bit Configuration for memory efficiency
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

Option 2: 16-bit Full Precision (High Accuracy Mode)

Best for users with 8GB+ VRAM (e.g., RTX 3060 12GB or higher). This provides the most precise mathematical reasoning.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "rimon-dutta/Rimon-Math-3B-V1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

Running Inference (Example)

Once the model is loaded, you can solve math problems using the standard Llama 3.2 chat template.

# Define your math problem
messages = [
    {"role": "system", "content": "You are a specialized math tutor. Explain step-by-step."},
    {"role": "user", "content": "If x + 1/x = 3, find the value of x^5 + 1/x^5."}
]

# Apply the chat template
inputs = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt"
).to(model.device)

# Generate the response
outputs = model.generate(
    **inputs, 
    max_new_tokens=1024, 
    temperature=0.1, # Low temperature is crucial for math accuracy
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

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

Troubleshooting Guide

1. 1.GPU Memory Error (OOM): If you get an "Out of Memory" error, restart your runtime and use Option 1 (4-bit).

1. 1.BitsAndBytes Issues: If loadin4bit fails, ensure you are running on a Linux-based environment (or WSL2 on Windows) and that your bitsandbytes is up to date:

pip install -U bitsandbytes

1. 1.CUDA Mismatch: If you encounter a runtime error regarding CUDA versions, reinstall PyTorch with the correct index URL:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Prompt Engineering Tips

Use a system prompt to control reasoning style Keep temperature between 0.1 – 0.3 for math tasks Always request step-by-step explanation Avoid ambiguous wording in problems

Author

<span style="color:#90ee90">

Rimon Dutta DevOps Engineer | AI & ML Learner Kotwali, Bangladesh

</span>