Update README.md

README.md

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----
-license: llama3.2
----
+---
+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
+---
+![python.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4nYxcbXSfu2Q0fIXul41e.gif)
+
+# **PyThagorean-10B**
+
+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.