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---
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
---
[](https://hf.co/QuantFactory)
# QuantFactory/Tulu-MathLingo-8B-GGUF
This is quantized version of [prithivMLmods/Tulu-MathLingo-8B](https://huggingface.co/prithivMLmods/Tulu-MathLingo-8B) created using llama.cpp
# Original Model Card
# 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.
---
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