Instructions to use sensenova/SenseNova-6.8-Code2Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sensenova/SenseNova-6.8-Code2Video with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="sensenova/SenseNova-6.8-Code2Video")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sensenova/SenseNova-6.8-Code2Video")
model = AutoModelForMultimodalLM.from_pretrained("sensenova/SenseNova-6.8-Code2Video", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sensenova/SenseNova-6.8-Code2Video with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sensenova/SenseNova-6.8-Code2Video"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sensenova/SenseNova-6.8-Code2Video",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/sensenova/SenseNova-6.8-Code2Video

SGLang

How to use sensenova/SenseNova-6.8-Code2Video with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sensenova/SenseNova-6.8-Code2Video" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sensenova/SenseNova-6.8-Code2Video",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sensenova/SenseNova-6.8-Code2Video" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sensenova/SenseNova-6.8-Code2Video",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use sensenova/SenseNova-6.8-Code2Video with Docker Model Runner:
```
docker model run hf.co/sensenova/SenseNova-6.8-Code2Video
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SenseNova 6.8 Code2Video

A code-driven video creation model, fine-tuned from Qwen3.8-27B

One model for math explainers / paper-craft films / isometric worlds / kinetic type / data stories / character animation / product promos

Launch film (90 s, with sound), made by SenseNova 6.8 Code2Video

We present SenseNova 6.8 Code2Video, a lightweight open model for code-driven video creation. The model builds and renders videos by generating executable code, and currently focuses on short videos implemented entirely in code. Preliminary results show good visual composition and motion choreography in motion design, suggesting the potential of lightweight models for programmatic video creation.

We chose code as the entry point to video creation partly for its ability to connect different models and creative tools. Beyond building and rendering visual content directly, code can call image generation, video generation, 3D modelling and video editing models and tools, chaining generation, editing and compositing into a complete creative workflow.

We believe code can become a common language between ideas and finished work. Our long-term goal is a model that coordinates different generative models and creative tools through code to create digital content across media. SenseNova 6.8 Code2Video is a first step in this direction. Next, we will scale the model further and extend these abilities to editing, compositing, animation and 3D content creation.

Released Resources

Resource	Location	Description
Model weights	This repository	Weights, config and tokenizer
Example trajectories	`trajectories/`	Message and tool logs with figures
Production skill	GitHub	Workflow, craft guides, rendering tools

Deployment

LightLLM

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  ghcr.io/modeltc/lightllm:main \
  python -m lightllm.server.api_server \
    --model_dir /model \
    --tp 4 \
    --max_req_total_len 524288 \
    --host 0.0.0.0 --port 8000

vLLM

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  vllm/vllm-openai:latest \
    --model /model \
    --tensor-parallel-size 4 \
    --max-model-len 524288 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 --port 8000

SGLang

docker run --rm --gpus all --ipc=host -p 8000:8000 \
  -e SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
  -v /path/to/SenseNova-6.8-Code2Video:/model \
  lmsysorg/sglang:latest \
  python -m sglang.launch_server \
    --model-path /model \
    --tp-size 4 \
    --context-length 524288 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 --port 8000

Recommended harness: Claude Code

A complete film is a long-horizon task: the model writes and renders code, reviews the frames and revises, turn after turn. SenseNova 6.8 Code2Video sustains runs of more than 10 hours and more than 2,000 turns. Configure Claude Code for runs of that length:

Setting	Value	Configuration
Context window	512K	`CLAUDE_CODE_MAX_CONTEXT_TOKENS=524288`
Auto-compaction	0.8	`CLAUDE_AUTOCOMPACT_PCT_OVERRIDE=80`
Max output	64K	`CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000`
Turns per run	2,000+	`--max-turns 2000` or more
Time per run	10 h+	`timeout 10h` or more

Set the context window explicitly, since Claude Code otherwise treats an unknown model as a 200K model. With compaction at 0.8 and 64K of output, every request stays within the 512K server limit.

Put the endpoint and these variables in a private settings file, and supply the credential through ANTHROPIC_AUTH_TOKEN or ANTHROPIC_API_KEY:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://your-endpoint.example.com",
    "ANTHROPIC_MODEL": "your-deployed-model-name",
    "CLAUDE_CODE_MAX_CONTEXT_TOKENS": "524288",
    "CLAUDE_AUTOCOMPACT_PCT_OVERRIDE": "80",
    "CLAUDE_CODE_MAX_OUTPUT_TOKENS": "64000"
  },
  "model": "your-deployed-model-name"
}

Avoid slow shell initialization: every Bash call starts a new shell.

Usage

Producing a film has three stages:

Stage	Performed by	Output
1. Prompt enhancement	A strong general model (we use Claude Opus 5.5) with the Enhance prompt template	`dense_prompt.md`
2. Production	SenseNova 6.8 Code2Video in Claude Code with the `code-video` skill	`out/final.mp4`, `SPEC.md`, `VERIFY.md`
3. Revision (optional)	The creator writes notes; SenseNova 6.8 Code2Video applies them in the same project	A new version of the film

Stage 2 with the settings above:

cd /path/to/task   # contains dense_prompt.md, assets/ and .claude/skills/code-video
claude -p --settings /path/to/private-settings.json --model your-deployed-model-name \
  --max-turns 2000 --permission-mode acceptEdits --allowedTools "Bash,Read,Write,Edit,Glob,Grep,Skill" \
  'Use the project code-video skill. Read .claude/skills/code-video/SKILL.md and dense_prompt.md, then the relevant craft guides. Produce the complete video and deliver out/final.mp4, SPEC.md and VERIFY.md.'

The run executes shell commands without asking, so start it in an isolated working environment. For a revision, run claude -p --continue in the same folder with the creator's notes as the prompt.

Copy the skill into .claude/skills/code-video unchanged and put task-specific requirements in the prompt. Setup steps and dependencies are in the GitHub repository.

Production trajectories

trajectories/all.openai.jsonl holds one film per line, in OpenAI messages format.

Each line contains messages, tools, images, enable_thinking and metadata. The messages include reasoning, tool calls and results, retries and compaction summaries. Images appear as <image> placeholders; the ordered images array gives their paths relative to trajectories/, and all 357 figures are in trajectories/figures/. Private paths and hosts are anonymized, so recorded commands are not directly replayable.

License

Released under the Apache License 2.0.

Contributors

We thank the following contributors for their work on SenseNova 6.8 Code2Video:

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Safetensors

Model size

27B params

Tensor type

BF16

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Quantizations

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