| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen3-1.7B |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - text-generation-inference |
| - code |
| - trl |
| --- |
| |
|  |
|
|
| # **Pyxidis-Manim-CodeGen-1.7B (Experimental)** |
|
|
| > **Pyxidis-Manim-CodeGen-1.7B** is an **experimental math animation coding model** fine-tuned on **Qwen/Qwen3-1.7B** using **Manim-CodeGen code traces**. |
| > It is specialized for **Python-based mathematical animations with Manim**, making it ideal for educators, researchers, and developers working on math visualization and animation pipelines. |
|
|
| > \[!note] |
| > GGUF: [https://huggingface.co/prithivMLmods/Pyxidis-Manim-CodeGen-1.7B-GGUF](https://huggingface.co/prithivMLmods/Pyxidis-Manim-CodeGen-1.7B-GGUF) |
|
|
| --- |
|
|
| ## **Key Features** |
|
|
| 1. **Manim-Specific Code Generation** |
| Trained on **Manim-CodeGen traces**, optimized for **Python-based animation scripting** of mathematical concepts and visual proofs. |
|
|
| 2. **Math + Code Synergy** |
| Generates step-by-step **math derivations with corresponding animation code**, bridging symbolic reasoning with visualization. |
|
|
| 3. **Animation Workflow Optimization** |
| Provides structured code for **scenes, transformations, graphs, and equations** in Manim, reducing boilerplate and debugging effort. |
|
|
| 4. **Python-Centric Reasoning** |
| Produces **clean, modular, and reusable Python code**, supporting educational and research-driven animation pipelines. |
|
|
| 5. **Structured Output Mastery** |
| Capable of outputting in **Python**, **Markdown**, and **LaTeX**, ideal for tutorials, educational notebooks, and automated video generation workflows. |
|
|
| 6. **Lightweight but Specialized** |
| Focused on **Manim coding efficiency** while maintaining a deployable footprint for **GPU clusters** and **research labs**. |
|
|
| --- |
|
|
| ## **Quickstart with Transformers** |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "prithivMLmods/Pyxidis-Manim-CodeGen-1.7B" |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| |
| prompt = "Write a Manim script to animate the Pythagorean theorem using squares on the triangle's sides." |
| |
| messages = [ |
| {"role": "system", "content": "You are a Python coding assistant specialized in Manim-based math animations."}, |
| {"role": "user", "content": prompt} |
| ] |
| |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=512 |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| print(response) |
| ``` |
|
|
| --- |
|
|
| ## **Intended Use** |
|
|
| * **Manim-based math animation coding** for research, teaching, and content creation |
| * **Educational visualization assistant** to convert math problems into animations |
| * **Python tutoring tool** for math-heavy animation workflows |
| * **Prototype generator** for interactive STEM video content |
|
|
| ## **Limitations** |
|
|
| * Experimental model – may generate code requiring manual debugging |
| * Limited to **Manim coding workflows**, not general-purpose code assistant |
| * May not handle **complex multi-scene projects** without iterative refinement |
| * Prioritizes structured math + animation reasoning, less optimized for general dialogue |
