license, language, base_model, pipeline_tag, library_name, tags

license	language	base_model	pipeline_tag	library_name	tags
apache-2.0	en	Qwen/Qwen3-1.7B	text-generation	transformers	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

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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.

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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)

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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