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