104 lines
3.6 KiB
Markdown
104 lines
3.6 KiB
Markdown
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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
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