Update README.md

README.md

@@ -46,6 +46,252 @@ inference:
     max_new_tokens: 512
 ---
 
-# 🎬 Qwen2.5-Coder-7B Fine-tuned for Manim Code Generation
+---
+
+# 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!
+
+---
 
-[Rest of README content here...]