Rimon-Math-3B-V1/README.md

---
license: mit
base_model: meta/llama-3.2-3b-instruct
tags:
- unsloth
- llama-3.2
- mathematics
- reasoning
- arithmetic
- fine-tuned
- rimon-dutta
- logic
- chain-of-thought
- open-r1
- conversational
- text-generation-inference
language:
- en
pipeline_tag: text-generation
library_name: transformers
datasets:
- open-r1/OpenR1-Math-220k
model_creator: Rimon Dutta
model_name: 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)
| Epoch | Step | Training Loss | Validation Loss | LR |
|------|------|--------------|----------------|--------------|
| 1.0  | 1000 | 0.7104       | 0.6952         | 1.5e-4       |
| 2.0  | 2000 | 0.5911       | 0.5843         | 5.0e-5       |
| 3.0  | 3000 | 0.5244       | 0.5102         | 1.0e-5       |

---

## 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:
```bash
pip install -U transformers torch accelerate bitsandbytes sentencepiece
```
| Component | Minimum (4-bit) | Recommended (16-bit) |
|----------|----------------|---------------------|
| GPU      | NVIDIA T4 / RTX 3050 (4GB VRAM) | RTX 3060 / A100 (12GB+ VRAM) |
| RAM      | 8 GB System RAM | 16 GB System RAM |
| CUDA     | 11.8 or higher | 12.1 or higher |

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

3. BitsAndBytes Issues:
If load_in_4bit fails, ensure you are running on a Linux-based environment (or WSL2 on Windows) and that your bitsandbytes is up to date:

```bash
pip install -U bitsandbytes
```
3. CUDA Mismatch:
If you encounter a runtime error regarding CUDA versions, reinstall PyTorch with the correct index URL:

```bash
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>