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Model: rimon-dutta/Rimon-Math-3B-V1 Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model: meta/llama-3.2-3b-instruct
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tags:
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- unsloth
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- llama-3.2
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- mathematics
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- reasoning
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- arithmetic
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- fine-tuned
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- rimon-dutta
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- logic
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- chain-of-thought
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- open-r1
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- conversational
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- text-generation-inference
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- open-r1/OpenR1-Math-220k
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model_creator: Rimon Dutta
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model_name: Rimon-Math-3B-V1
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---
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# Rimon-Math-3B-V1
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**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).
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## Highlights
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- **Reasoning Focused:** Trained specifically to break down complex problems into logical steps.
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- **Lightweight & Efficient:** Optimized for consumer-grade GPUs (T4, RTX 3060+) and edge deployment.
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- **High Compatibility:** Works seamlessly with `transformers`, `vLLM`, and supports `GGUF` conversion for local use.
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---
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## Model Capabilities
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The model is fine-tuned to handle various mathematical domains:
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- **Algebra:** Solving equations, inequalities, and system of equations.
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- **Calculus:** Derivatives, integrals, and limit problems.
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- **Geometry & Trigonometry:** Properties of shapes and trigonometric identities.
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- **Logic & Arithmetic:** Multi-step word problems and sequence analysis.
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---
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### Training Metrics (Approximation)
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| Epoch | Step | Training Loss | Validation Loss | LR |
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|------|------|--------------|----------------|--------------|
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| 1.0 | 1000 | 0.7104 | 0.6952 | 1.5e-4 |
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| 2.0 | 2000 | 0.5911 | 0.5843 | 5.0e-5 |
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| 3.0 | 3000 | 0.5244 | 0.5102 | 1.0e-5 |
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---
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## Usage Guide
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## Installation & Dependencies
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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:
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```bash
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pip install -U transformers torch accelerate bitsandbytes sentencepiece
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```
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| Component | Minimum (4-bit) | Recommended (16-bit) |
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|----------|----------------|---------------------|
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| GPU | NVIDIA T4 / RTX 3050 (4GB VRAM) | RTX 3060 / A100 (12GB+ VRAM) |
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| RAM | 8 GB System RAM | 16 GB System RAM |
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| CUDA | 11.8 or higher | 12.1 or higher |
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## How to Use the Model
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You can load the model in two different modes depending on your hardware resources.
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# Option 1: 4-bit Quantization (Low VRAM Mode)
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Best for users on Google Colab (Free T4) or laptops with limited GPU memory. This uses only ~3.5 GB of VRAM.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_id = "rimon-dutta/Rimon-Math-3B-V1"
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# 4-bit Configuration for memory efficiency
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=bnb_config,
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device_map="auto",
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trust_remote_code=True
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)
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```
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# Option 2: 16-bit Full Precision (High Accuracy Mode)
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Best for users with 8GB+ VRAM (e.g., RTX 3060 12GB or higher). This provides the most precise mathematical reasoning.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "rimon-dutta/Rimon-Math-3B-V1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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```
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# Running Inference (Example)
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Once the model is loaded, you can solve math problems using the standard Llama 3.2 chat template.
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```python
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# Define your math problem
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messages = [
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{"role": "system", "content": "You are a specialized math tutor. Explain step-by-step."},
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{"role": "user", "content": "If x + 1/x = 3, find the value of x^5 + 1/x^5."}
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]
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# Apply the chat template
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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# Generate the response
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.1, # Low temperature is crucial for math accuracy
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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# Troubleshooting Guide
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1. GPU Memory Error (OOM):
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If you get an "Out of Memory" error, restart your runtime and use Option 1 (4-bit).
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3. BitsAndBytes Issues:
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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:
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```bash
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pip install -U bitsandbytes
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```
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3. CUDA Mismatch:
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If you encounter a runtime error regarding CUDA versions, reinstall PyTorch with the correct index URL:
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```bash
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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```
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# Prompt Engineering Tips
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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
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## Author
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<span style="color:#90ee90">
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Rimon Dutta
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DevOps Engineer | AI & ML Learner
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Kotwali, Bangladesh
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</span>
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139
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 July 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content'] %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>
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" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython
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" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "
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"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023
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" }}
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{{- "Today Date: " + date_string + "
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" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.
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" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "
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" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content'] %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>
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' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.
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" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.
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" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "
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" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>
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' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>
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|
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' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>
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" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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||||||
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{{- '<|start_header_id|>assistant<|end_header_id|>
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|
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' }}
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{%- endif %}
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37
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "float16",
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 8192,
|
||||||
|
"max_position_embeddings": 131072,
|
||||||
|
"mlp_bias": false,
|
||||||
|
"model_type": "llama",
|
||||||
|
"num_attention_heads": 24,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": 128004,
|
||||||
|
"pretraining_tp": 1,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_parameters": {
|
||||||
|
"factor": 32.0,
|
||||||
|
"high_freq_factor": 4.0,
|
||||||
|
"low_freq_factor": 1.0,
|
||||||
|
"original_max_position_embeddings": 8192,
|
||||||
|
"rope_theta": 500000.0,
|
||||||
|
"rope_type": "llama3"
|
||||||
|
},
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"transformers_version": "5.0.0",
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 128256
|
||||||
|
}
|
||||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
@@ -0,0 +1,11 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": 128001,
|
||||||
|
"max_length": 131072,
|
||||||
|
"pad_token_id": 128004,
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_p": 0.9,
|
||||||
|
"transformers_version": "5.0.0"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:5c0fbf70f907aa2e71b76a1067bfd36982f4574fe372c3690254551fe37183cf
|
||||||
|
size 6425528856
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
||||||
|
size 17209920
|
||||||
0
tokenizer.model.bak
Normal file
0
tokenizer.model.bak
Normal file
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": "<|begin_of_text|>",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"eos_token": "<|end_of_text|>",
|
||||||
|
"is_local": true,
|
||||||
|
"model_input_names": [
|
||||||
|
"input_ids",
|
||||||
|
"attention_mask"
|
||||||
|
],
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|finetune_right_pad_id|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"tokenizer_class": "TokenizersBackend",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user