PyThagorean-1B

PyThagorean [Python + Math] is a Python and mathematics-based model designed to solve mathematical problems using Python libraries and coding. It has been fine-tuned on 1.5 million entries and is built on LLaMA's architecture. The model supports different parameter sizes, including 10B, 3B, and 1B (Tiny). These instruction-tuned, text-only models are optimized for multilingual dialogue use cases, including agent-based retrieval and summarization tasks. PyThagorean leverages an auto-regressive language model that uses an optimized transformer architecture. The tuned versions employ supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Use with transformers

Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

import transformers
import torch

model_id = "prithivMLmods/PyThagorean-Tiny"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are the helpful assistant. Solve the mathematical problem in Python programming."},
    {"role": "user", "content": "Find all real numbers $x$ such that \[\frac{x^3+2x^2}{x^2+3x+2} + x = -6.\]Enter all the solutions, separated by commas."},
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Intended Use

1. Mathematical Problem Solving:
   PyThagorean is designed for solving complex mathematical problems, including algebra, calculus, trigonometry, and more, by leveraging Python-based libraries. It is ideal for educational tools, tutoring platforms, and automated math assistants.

2. Python Code Generation:
   The model generates Python code snippets for mathematical computations, simulations, and problem-solving, making it valuable for developers, researchers, and students.

3. Multilingual Dialogue Systems:
   With support for multiple languages, PyThagorean can assist users worldwide in understanding and solving mathematical problems through dialogue-based interfaces.

4. Instruction-Following Tasks:
   The model excels at adhering to precise mathematical instructions and delivering accurate, step-by-step solutions for problems embedded in text.

5. Agent-Based Knowledge Retrieval:
   PyThagorean can retrieve and summarize mathematical concepts or problem-solving techniques, enabling quick access to relevant knowledge for educational and research purposes.

6. Educational Content Creation:
   It generates educational content such as example problems, solutions, and Python-based tutorials, aiding teachers and content creators.

7. Summarization and Explanation:
   The model provides clear explanations and breakdowns of mathematical solutions, helping users understand the rationale and process behind the answers.

Limitations

1. Performance on Ambiguous Instructions:
   The model may struggle with ambiguous, vague, or poorly framed mathematical instructions, potentially leading to incorrect or incomplete solutions.

2. Edge Cases and Special Scenarios:
   For highly specialized or niche mathematical problems, especially those not commonly encountered in training data, the model's performance may degrade.

3. Errors in Multi-Step Reasoning:
   While trained on reasoning datasets, the model may sometimes produce incorrect results for multi-step or highly complex reasoning tasks, particularly if intermediate steps are not explicitly defined.

4. Bias Toward Common Solutions:
   The model may favor standard or commonly used approaches to mathematical problems, potentially missing creative or less conventional methods of solution.

5. Resource Intensity:
   As a large-scale model, PyThagorean requires significant computational resources, including high-end GPUs, for efficient inference and deployment.

6. Context Window Limitations:
   The model's finite context window may lead to incomplete understanding or truncated responses for problems requiring extensive context or lengthy input.

7. Handling of Non-Mathematical Queries:
   While capable of engaging in general conversations, its performance for non-mathematical tasks may not match models specifically tuned for broader use cases.

8. Dependency on Python Libraries:
   Generated solutions may rely on specific Python libraries or functions, which users must have installed and configured correctly to execute the code successfully.