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---
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 |