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
Key Features
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Manim-Specific Code Generation Trained on Manim-CodeGen traces, optimized for Python-based animation scripting of mathematical concepts and visual proofs.
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Math + Code Synergy Generates step-by-step math derivations with corresponding animation code, bridging symbolic reasoning with visualization.
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Animation Workflow Optimization Provides structured code for scenes, transformations, graphs, and equations in Manim, reducing boilerplate and debugging effort.
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Python-Centric Reasoning Produces clean, modular, and reusable Python code, supporting educational and research-driven animation pipelines.
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Structured Output Mastery Capable of outputting in Python, Markdown, and LaTeX, ideal for tutorials, educational notebooks, and automated video generation workflows.
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Lightweight but Specialized Focused on Manim coding efficiency while maintaining a deployable footprint for GPU clusters and research labs.
Quickstart with Transformers
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