|
--- |
|
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 |
|
|
|
 |
|
|
|
### **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. |
|
|
|
--- |
