| ---
|
| license: apache-2.0
|
| base_model: Qwen/Qwen3-8B
|
| library_name: transformers
|
| pipeline_tag: text-generation
|
| language:
|
| - en
|
| tags:
|
| - safetensors
|
| - dpo
|
| - preference
|
| - rlhf
|
| - manim
|
| - manim-voiceover
|
| - aos
|
| - code-generation
|
| - animation
|
| - merged
|
| ---
|
|
|
| # AOS Qwen3 8B Narrated (DPO Aligned - Merged Safetensors)
|
|
|
| Direct Preference Optimization (DPO) aligned full bf16 model for **Manim Community Edition** mathematical and educational animation synthesis with synchronized voiceover narration.
|
|
|
| **Model Repository:** [nabin2004/AOS-qwen3-8b-narrated-merged](https://huggingface.co/nabin2004/AOS-qwen3-8b-narrated-merged)
|
|
|
| ---
|
|
|
| ## Lineage & Provenance
|
|
|
| | Role | Artifact / Repository | Description |
|
| |---|---|---|
|
| | **Base LLM** | [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) | Base causal foundation model |
|
| | **SFT Prior** | [`nabin2004/AOS-qwen3-8b-narrated-adapter`](https://huggingface.co/nabin2004/AOS-qwen3-8b-narrated-adapter) | Continued SFT on 400 synchronized educational voiceover trajectories |
|
| | **DPO Adapter** | [`nabin2004/AOS-qwen3-8b-narrated-dpo`](https://huggingface.co/nabin2004/AOS-qwen3-8b-narrated-dpo) | Direct Preference Optimization adapter ($\beta=0.1$) |
|
| | **Merged Weights** | [`nabin2004/AOS-qwen3-8b-narrated-merged`](https://huggingface.co/nabin2004/AOS-qwen3-8b-narrated-merged) | Full unquantized bfloat16 Safetensors weights (this repo) |
|
| | **GGUF / Ollama** | [`nabin2004/AOS-qwen3-8b-narrated-gguf`](https://huggingface.co/nabin2004/AOS-qwen3-8b-narrated-gguf) | Multi-quantized GGUF (`Q4_K_M`, `Q8_0`) for Ollama & llama.cpp |
|
|
|
| ---
|
|
|
| ## Alignment Objective
|
|
|
| The model was aligned with Direct Preference Optimization (DPO) to strongly prefer generating voiceover-synchronized educational animations:
|
| - **Chosen**: Clean `VoiceoverScene` scripts with speech services (`AOSSpeechService` / `GTTSService`), animation duration tracking (`run_time=tracker.duration`), millisecond-accurate `<bookmark mark='...'/>` tags, and natural phonetic spoken narration.
|
| - **Rejected**: Silent, un-narrated standard `Scene` code.
|
|
|
| ---
|
|
|
| ## Canonical Code Pattern
|
|
|
| ```python
|
| from manim import *
|
| from manim_voiceover import VoiceoverScene
|
| from manim_voiceover.services.gtts import GTTSService
|
|
|
| class SigmoidExplanation(VoiceoverScene):
|
| def construct(self):
|
| # Configure speech service
|
| self.set_speech_service(GTTSService())
|
|
|
| title = Title("The Sigmoid Activation Function")
|
| ax = Axes(x_range=[-6, 6, 2], y_range=[-0.2, 1.2, 0.5])
|
| curve = ax.plot(lambda x: 1 / (1 + np.exp(-x)), color=BLUE)
|
| dot = Dot(ax.c2p(0, 0.5), color=RED)
|
|
|
| with self.voiceover(
|
| text="Let's visualize the sigmoid function. <bookmark mark='AXES'/> We begin by setting up our coordinate system, <bookmark mark='CURVE'/> plotting the characteristic S-shaped curve, <bookmark mark='DOT'/> and marking the midpoint inflection at zero, point five."
|
| ) as tracker:
|
| self.play(Write(title))
|
| self.wait_until_bookmark("AXES")
|
| self.play(Create(ax))
|
| self.wait_until_bookmark("CURVE")
|
| self.play(Create(curve))
|
| self.wait_until_bookmark("DOT")
|
| self.play(FadeIn(dot), run_time=tracker.duration)
|
| self.wait(1)
|
| ```
|
|
|
| ---
|
|
|
| ## Quickstart Usage
|
|
|
| ### Hugging Face Transformers
|
|
|
| ```python
|
| import torch
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| model_id = "nabin2004/AOS-qwen3-8b-narrated-merged"
|
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
|
| model = AutoModelForCausalLM.from_pretrained(
|
| model_id,
|
| torch_dtype=torch.bfloat16,
|
| device_map="auto",
|
| trust_remote_code=True,
|
| )
|
|
|
| prompt = "Create a narrated Manim animation explaining the Fourier Transform with voiceover bookmarks."
|
| messages = [
|
| {{"role": "system", "content": "You are an expert mathematical animation assistant specializing in Manim Community Edition and voiceover narration with manim-voiceover. You write complete, self-contained, fully executable Python scripts inheriting from VoiceoverScene."}},
|
| {{"role": "user", "content": prompt}}
|
| ]
|
|
|
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
| inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
|
|
| outputs = model.generate(
|
| **inputs,
|
| max_new_tokens=2048,
|
| temperature=0.2,
|
| top_p=0.95,
|
| pad_token_id=tokenizer.eos_token_id,
|
| )
|
| print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
|
| ```
|
|
|
| ### High-Throughput Cloud Serving with vLLM
|
|
|
| Serve as a high-performance OpenAI-compatible endpoint:
|
|
|
| ```bash
|
| vllm serve nabin2004/AOS-qwen3-8b-narrated-merged \
|
| --port 8000 \
|
| --max-model-len 8192 \
|
| --trust-remote-code
|
| ```
|
|
|
| ---
|
|
|
| ## Citation & Acknowledgments
|
|
|
| Part of the **AOS (Agentic Orchestration System)** project for multi-agent educational video synthesis.
|
| - Base Model: Alibaba Cloud Qwen Team (`Qwen/Qwen3-8B`)
|
| - Animation Engine: Manim Community Edition & `manim-voiceover`
|
|
|
