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Model: rimon-dutta/Rimon-Math-3B-V1 Source: Original Platform
2026-04-11 05:22:56 +08:00
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+---
+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>