初始化项目，由ModelHub XC社区提供模型
Model: prithivMLmods/PyThagorean-Tiny Source: Original Platform
This commit is contained in:
ModelHub XC
103
README.md
Normal file
103
README.md
Normal file
@@ -0,0 +1,103 @@
|
||||
---
|
||||
license: llama3.2
|
||||
datasets:
|
||||
- prithivMLmods/PyThagoreans-Merged
|
||||
language:
|
||||
- en
|
||||
base_model:
|
||||
- meta-llama/Llama-3.2-1B-Instruct
|
||||
pipeline_tag: text-generation
|
||||
library_name: transformers
|
||||
tags:
|
||||
- math
|
||||
- coder
|
||||
- problem-solve
|
||||
- open_coder
|
||||
---
|
||||
|
||||
|
||||
# **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`.
|
||||
|
||||
```python
|
||||
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.
|
||||
Reference in New Issue
Block a user