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Model: Harish102005/Qwen2.5-Coder-7B-manim
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---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B
tags:
- code-generation
- manim
- python
- animation
- mathematics
- unsloth
- qlora
- text-generation-inference
- transformers
- peft
- lora
datasets:
- dalle2/3blue1brown-manim
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-Coder-7B-manim
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: 3blue1brown-manim
type: dalle2/3blue1brown-manim
metrics:
- type: loss
value: 0.553
name: Final Training Loss
widget:
- text: "Generate Manim code for the following task: Create a blue circle"
example_title: "Simple Shape"
- text: "Generate Manim code for the following task: Draw a sine wave animation"
example_title: "Mathematical Function"
- text: "Generate Manim code for the following task: Show the Pythagorean theorem"
example_title: "Mathematical Formula"
inference:
parameters:
temperature: 0.3
top_p: 0.9
max_new_tokens: 512
---
---
# Qwen2.5-Coder-7B-Manim
[![Model on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/model-on-hf-md.svg)](https://huggingface.co/Harish102005/Qwen2.5-Coder-7B-manim)
[![Base Model](https://img.shields.io/badge/Base_Model-Qwen2.5--Coder--7B-green)](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
**Generate Manim (Mathematical Animation Engine) Python code from natural language descriptions!**
Fine-tuned on **2,407 examples** from the 3Blue1Brown Manim dataset using **QLoRA** with Unsloth.
---
## 🚀 Quick Start
### Installation
```bash
pip install unsloth transformers accelerate
```
### Load Model
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Harish102005/Qwen2.5-Coder-7B-manim",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
```
### Generate Manim Code
```python
# Alpaca-style prompt template
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
prompt = "Create a blue circle that grows to twice its size"
inputs = tokenizer([
alpaca_prompt.format(
"Generate Manim code for the following task:",
prompt,
""
)
], return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.3,
top_p=0.9,
repetition_penalty=1.1,
do_sample=True,
)
generated_code = tokenizer.decode(outputs, skip_special_tokens=True)
print(generated_code.split("### Response:")[-1].strip())
```
---
### Helper Function
```python
def generate_manim_code(prompt, max_tokens=512):
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
formatted_prompt = alpaca_prompt.format(
"Generate Manim code for the following task:",
prompt,
""
)
inputs = tokenizer([formatted_prompt], return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=0.3,
top_p=0.9,
repetition_penalty=1.1,
do_sample=True,
)
generated_text = tokenizer.decode(outputs, skip_special_tokens=True)
if "### Response:" in generated_text:
code = generated_text.split("### Response:")[-1].strip()
if "### Instruction:" in code:
code = code.split("### Instruction:")[0].strip()
return code
return generated_text
# Example
code = generate_manim_code("Create a rotating square")
print(code)
```
---
## 📊 Example Outputs
### 1. Color-Changing Rotation
**Prompt:** `"Create a rotating square that changes color from blue to red"`
```python
from manim import *
class MyScene(Scene):
def construct(self):
square = Square(color=BLUE)
self.add(square)
self.play(square.animate.rotate(PI), run_time=2)
square.set_color(RED)
```
### 2. Mathematical Function
**Prompt:** `"Draw a sine wave from 0 to 2π with animation"`
```python
from manim import *
class MyScene(Scene):
def construct(self):
axes = Axes(x_range=[0, 2*PI], y_range=[-1, 1])
graph = axes.plot(lambda x: np.sin(x), color=BLUE)
self.add(axes, graph)
```
### 3. Formula Display
**Prompt:** `"Show the equation E=mc² and fade it in"`
```python
from manim import *
class MyScene(Scene):
def construct(self):
e_mc_squared = MathTex("E=mc^2")
self.play(Write(e_mc_squared))
self.wait()
```
---
## 📈 Model Details
* **Base Model:** [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
* **Fine-tuning Method:** QLoRA (4-bit) with [Unsloth](https://github.com/unslothai/unsloth)
* **Dataset:** [dalle2/3blue1brown-manim](https://huggingface.co/datasets/dalle2/3blue1brown-manim)
* **Dataset Size:** 2,407 prompt-code pairs
* **Final Training Loss:** 0.553
* **Model Type:** Qwen2ForCausalLM
* **Parameters:** ~7.6B (base), Trainable: 40.4M (0.53%)
### Hyperparameters
| Parameter | Value |
| ------------------- | ------------------------------------------------------------- |
| LoRA Rank (r) | 16 |
| LoRA Alpha | 16 |
| LoRA Dropout | 0.0 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Max Sequence Length | 2048 |
| Precision | BFloat16 |
| Quantization | 4-bit NF4 (double quantization) |
---
## 🎯 Use Cases
* Generate educational animations (math tutorials, visualizations)
* Rapid prototyping of visual content in Manim
* Learning Manim syntax and animation techniques
* Content automation (batch animation generation)
---
## ⚠️ Limitations
* Primarily for **2D Manim animations**; may struggle with complex 3D scenes
* Training data limited to **3Blue1Brown patterns** (2,407 examples)
* Minor manual corrections may be needed for complex animations
* Advanced Manim features (custom shaders, complex mobjects) not fully supported
---
## 🔧 Advanced Usage
### Streaming Output
```python
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512, temperature=0.3)
```
### Batch Generation
```python
prompts = ["Create a blue circle", "Draw a red square", "Show a green triangle"]
for prompt in prompts:
code = generate_manim_code(prompt)
print(f"Prompt: {prompt}\n{code}\n{'-'*60}")
```
---
## 🙏 Acknowledgments
* **Base Model:** [Qwen Team](https://github.com/QwenLM/Qwen)
* **Dataset:** [dalle2](https://huggingface.co/datasets/dalle2)
* **Training Framework:** [Unsloth](https://github.com/unslothai/unsloth)
* **Inspiration:** [3Blue1Brown](https://www.3blue1brown.com/) and the Manim Community
---
---
✅ **Star this model** if you find it useful!
---

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