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# **PyThagorean-10B**
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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.
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# **PyThagorean-10B**
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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.
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# **Use with transformers**
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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.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import transformers
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import torch
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model_id = "prithivMLmods/PyThagorean-10B"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are the helpful assistant. Solve the mathematical problem in Python programming."},
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{"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."},
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]
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outputs = pipeline(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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