Tulu-MathLingo-8B-GGUF 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	1.78 kB	Tracks large files using Git LFS.	Uploaded
README.md	449 Bytes	Basic information about the Tulu-MathLingo model.	Updated
Tulu-MathLingo-8B.F16.gguf	16.1 GB	Full precision (FP16) model weights.	Uploaded (LFS)
Tulu-MathLingo-8B.Q4_K_M.gguf	4.92 GB	Quantized Q4_K_M model weights.	Uploaded (LFS)
Tulu-MathLingo-8B.Q5_K_M.gguf	5.73 GB	Quantized Q5_K_M model weights.	Uploaded (LFS)
Tulu-MathLingo-8B.Q8_0.gguf	8.54 GB	Quantized Q8_0 model weights.	Uploaded (LFS)
config.json	29 Bytes	Minimal configuration file.	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.

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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.

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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.

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Usage

Loading the Model

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")

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Math Word Problem

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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Run with Ollama [ Ollama Run ]

Overview

Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.

Table of Contents

• Download and Install Ollama
• Steps to Run GGUF Models
  • 1. Create the Model File
  • 2. Add the Template Command
  • 3. Create and Patch the Model
• Running the Model
• Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Steps to Run GGUF Models

1. Create the Model File

First, create a model file and name it appropriately. For example, you can name your model file metallama.

2. Add the Template Command

In your model file, include a FROM line that specifies the base model file you want to use. For instance:

FROM Llama-3.2-1B.F16.gguf

Ensure that the model file is in the same directory as your script.

3. Create and Patch the Model

Open your terminal and run the following command to create and patch your model:

ollama create metallama -f ./metallama

Once the process is successful, you will see a confirmation message.

To verify that the model was created successfully, you can list all models with:

ollama list

Make sure that metallama appears in the list of models.

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Running the Model

To run your newly created model, use the following command in your terminal:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

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Conclusion

With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.

• This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.

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