---
license: creativeml-openrail-m
datasets:
- microsoft/orca-math-word-problems-200k
language:
- en
base_model:
- allenai/Llama-3.1-Tulu-3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- math
- tulu
- trl
- llama
- text-generation-inference
- math_lingo
---

# Tulu-MathLingo-8B Model Files

The **Tulu-MathLingo-8B** model is a fine-tuned version of **meta-llama/Llama-3.1-8B**, optimized for solving mathematical word problems and reasoning tasks in English and the Tulu language. The model integrates advanced language understanding and reasoning capabilities with a focus on providing solutions to math-related queries.

| **File Name**                     | **Size**     | **Description**                                 | **Upload Status** |
|-----------------------------------|--------------|------------------------------------------------|-------------------|
| `.gitattributes`                  | 1.57 kB     | Configures LFS tracking for large files.        | Updated           |
| `README.md`                       | 292 Bytes   | Basic details about the uploaded model.         | Updated           |
| `config.json`                     | 988 Bytes   | Contains model architecture and metadata.       | Uploaded          |
| `generation_config.json`          | 241 Bytes   | Parameters for text generation (e.g., length, temperature). | Uploaded          |
| `model-00001-of-00004.safetensors`| 4.98 GB     | Part 1 of model weights.                        | Uploaded (LFS)    |
| `model-00002-of-00004.safetensors`| 5 GB        | Part 2 of model weights.                        | Uploaded (LFS)    |
| `model-00003-of-00004.safetensors`| 4.92 GB     | Part 3 of model weights.                        | Uploaded (LFS)    |
| `model-00004-of-00004.safetensors`| 1.17 GB     | Part 4 of model weights.                        | Uploaded (LFS)    |
| `model.safetensors.index.json`    | 25.4 kB     | Index file for multi-part model weights.        | Uploaded          |
| `special_tokens_map.json`         | 462 Bytes   | Maps special tokens (e.g., `<PAD>`, `<EOS>`).   | Uploaded          |
| `tokenizer.json`                  | 17.2 MB     | Full tokenizer configuration.                   | Uploaded (LFS)    |
| `tokenizer_config.json`           | 57.6 kB     | Metadata for tokenizer usage.                   | Uploaded          |
### Sample Solve

![xvxv.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vX8m-ltsacAztTF9SqDxB.png)

### **Key Features**

1. **Multilingual Math Reasoning:**  
   - Designed for solving complex math problems in **English** and **Tulu**.  

2. **Text Generation:**  
   - Generates detailed and contextually accurate text responses.

3. **Fine-Tuned Specializations:**  
   - Trained on the **microsoft/orca-math-word-problems-200k** dataset for word problem-solving.  

4. **Special Token Mapping:**  
   - Configured to use tokens for specific functions such as `<PAD>` and `<EOS>` effectively.

5. **Secure and Efficient Storage:**  
   - Model weights are stored in the **Safetensors** format for secure and faster inference.

6. **Large Parameter Size:**  
   - 8.03 billion parameters enable handling complex queries and multi-turn conversations.

---

### **Training Details**

- **Base Model:** [meta-llama/Llama-3.1-8B](#)  
- **Fine-Tuned:**  
   - Through multiple stages: **SFT (Supervised Fine-Tuning)** and **DPO (Direct Preference Optimization)**.

- **Dataset:**  
   - Trained on **200k word problems** from the **Microsoft Orca Math Word Problems Dataset**.

- **Model Size:**  
   - 8.03B parameters, optimized for **FP16** tensor type.

---

### **Applications**

1. **Mathematical Word Problems:**  
   - Solve structured or unstructured math problems in natural language.

2. **Conversational AI for Math:**  
   - Engage users in interactive dialogues focused on math and logic reasoning.

3. **Multilingual Support:**  
   - Supports queries in **Tulu** and **English**, enhancing accessibility.

4. **Education Tools:**  
   - Useful in tutoring systems for math, helping students with problem-solving.

---

### **Usage**

#### **Loading the Model**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Tulu-MathLingo-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="fp16")
```

---

##### **Math Word Problem**
```python
query = "If a train travels 60 miles in 2 hours, what is its average speed?"
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Answer:", response)
```
### **Performance Requirements**

- **Hardware:**  
   - Requires a GPU with at least **24GB VRAM** for optimal performance due to model size and FP16 usage.

- **Optimization:**  
   - Use mixed precision (`fp16`) for reduced memory footprint.  
   - Split inference across multiple GPUs if necessary.

---