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SenseNova 6.8 Code2Video · Every frame is code. · 把脑洞，码成片

SenseNova 6.8 Code2Video

-

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

+

A code-driven video generation 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
-
GitHub License: Apache 2.0 Params: 27B Context: 512K Discord: OpenSenseNova
+
GitHub License: Apache 2.0 Params: 27B Context: 512K Harness: Claude Code
SenseNova 6.8 Code2Video launch film

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. +**SenseNova 6.8 Code2Video is not an image or video generation model and never outputs pixels.** It is a language model that makes videos by writing code. Given a production brief, it programs the film's visuals, motion and sound (Canvas, SVG/HTML, Three.js, WebGL or Manim), renders the program frame by frame, reviews the rendered frames, fixes the code and delivers an MP4. -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. +Because every film is produced through code, the model must run inside an agent harness that executes its code and shows it the rendered frames. Use it with **Claude Code** and our fixed **`code-video` skill**, the setup in which its production trajectories were recorded. ## Released Resources +We release the model weights, six example production trajectories, and the accompanying `code-video` skill. + | Resource | Location | Description | |---|---|---| -| **Model weights** | [This repository](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/tree/main) | Weights, config and tokenizer | -| **Example trajectories** | [`trajectories/`](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/blob/main/trajectories/all.openai.jsonl) | Message and tool logs with figures | -| **Production skill** | [GitHub](https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video/tree/main/skills/code-video) | Workflow, craft guides, rendering tools | +| **Model weights** | [This repository](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/tree/main) | Fine-tuned from Qwen3.8-27B | +| **Example trajectories** | [`trajectories/`](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/blob/main/trajectories/all.openai.jsonl) | Six full production runs ([details](#production-trajectories)) | +| **Production skill** | [GitHub](https://github.com/syssysyssy/sensenova-codevideo/tree/main/skills/code-video) | Workflow, craft guides, rendering tools | + +For setup instructions, see [Deployment](#deployment) and [Recommended harness](#recommended-harness-claude-code). ## Deployment +| Item | Setting | +|---|---| +| Inference engines | [LightLLM](https://github.com/ModelTC/LightLLM), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang) | +| Hardware | 4 × H800 (80 GB), tensor parallelism 4 | +| Maximum sequence length | 512K: 524,288 tokens, prompt plus output | +| API for the harness | Anthropic Messages-compatible endpoint (`/v1/messages`) with tool use and streaming; we put a thin pass-through gateway in front of the inference server | +| Sampling | `temperature` 1.0, `top_p` 0.95, `top_k` 20, applied on the server side | + +Replace `/path/to/SenseNova-6.8-Code2Video` with your local model directory. ### [LightLLM](https://github.com/ModelTC/LightLLM) ```bash +docker pull ghcr.io/modeltc/lightllm:main + docker run --rm --gpus all --ipc=host -p 8000:8000 \ -v /path/to/SenseNova-6.8-Code2Video:/model \ ghcr.io/modeltc/lightllm:main \ @@ -59,6 +72,8 @@ docker run --rm --gpus all --ipc=host -p 8000:8000 \ ### [vLLM](https://github.com/vllm-project/vllm) ```bash +docker pull vllm/vllm-openai:latest + 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 \ @@ -75,6 +90,8 @@ docker run --rm --gpus all --ipc=host -p 8000:8000 \ ### [SGLang](https://github.com/sgl-project/sglang) ```bash +docker pull lmsysorg/sglang:latest + 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 \ @@ -90,7 +107,9 @@ docker run --rm --gpus all --ipc=host -p 8000:8000 \ ## 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: +The model expects Claude Code's tool interface: Bash, Read, Write and Edit, with rendered frames read back as images through Read. + +A complete film is a long-horizon task: the model plans the film, writes and renders code, reviews the frames and revises, step after step. SenseNova 6.8 Code2Video sustains such runs for **more than 10 hours and more than 2,000 steps**. Configure Claude Code to give it room for runs of that length: | Setting | Value | Configuration | |---|---|---| @@ -100,9 +119,13 @@ A complete film is a long-horizon task: the model writes and renders code, revie | 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. +How the settings fit together: -Put the endpoint and these variables in a private settings file, and supply the credential through `ANTHROPIC_AUTH_TOKEN` or `ANTHROPIC_API_KEY`: +- **Context and compaction.** Without `CLAUDE_CODE_MAX_CONTEXT_TOKENS`, Claude Code assumes a 200K window for a model it does not know. With 512K and a threshold of 0.8, compaction starts at about 403K tokens (80 % of the window minus a 20K output reserve), so a run continues past the window instead of stopping. +- **Output.** 64K replaces Claude Code's 32K default for an unknown model. About 403K tokens of context plus 64K of output keep every request within the 524,288-token server limit. +- **Turns and time.** With compaction in place, a run can go beyond 2,000 steps and 10 hours; set both limits to at least these values so that a film is not cut off before delivery. + +Put the endpoint and the settings in a private settings file, and supply the credential through `ANTHROPIC_AUTH_TOKEN` or `ANTHROPIC_API_KEY` in the environment: ```json { @@ -117,7 +140,10 @@ Put the endpoint and these variables in a private settings file, and supply the } ``` -Avoid slow shell initialization: every Bash call starts a new shell. +Also: + +- **Image reading.** The model checks its own stills and contact sheets with Read, so the harness must be able to read image files. +- **Shell startup.** Keep the shell profile light: every Bash call starts a shell, and a heavy login profile added about 11 s per call in our setup (0.2 s without it). ## Usage @@ -125,42 +151,38 @@ 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](https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video/blob/main/prompts/enhance_prompt.md) | `dense_prompt.md` | -| 2. Production | SenseNova 6.8 Code2Video in Claude Code with the `code-video` skill | `out/final.mp4`, `SPEC.md`, `VERIFY.md` | +| 1. Prompt enhancement | A strong general model (we use Claude Opus 5.5) with the [dense prompt template](https://github.com/syssysyssy/sensenova-codevideo/blob/main/prompts/dense_prompt_template.md) | `prompt_dense.md` | +| 2. Production | SenseNova 6.8 Code2Video in Claude Code with the fixed `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: ```bash -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 \ +cd /path/to/task # contains prompt_dense.md, assets/ and .claude/skills/code-video +timeout 10h 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.' + 'Use the project code-video skill. Read .claude/skills/code-video/SKILL.md and prompt_dense.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](https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video). +The skill is copied into the task folder (`.claude/skills/code-video`) as it is; task-specific requirements belong in the prompt, never in the skill. Setup steps and dependencies are in the [GitHub repository](https://github.com/syssysyssy/sensenova-codevideo). ## Production trajectories -[`trajectories/all.openai.jsonl`](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/blob/main/trajectories/all.openai.jsonl) holds one film per line, in OpenAI messages format. +[`trajectories/all.openai.jsonl`](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/blob/main/trajectories/all.openai.jsonl) holds the complete production trajectories of six films, one film per line, in OpenAI messages format. + +| Line | `metadata.case_id` | Film | +|---|---|---| +| 0 | `fourier_light_painting` | Fourier Light Painting | +| 1 | `dpo_forest_landslide` | Can a Forest Hide a Landslide? | +| 2 | `signal_noise_shape` | Signal / Noise / Shape | +| 3 | `v1_fx09_cmyk_halftone` | CMYK Halftone | +| 4 | `v1_geometry_risograph` | Geometry / Risograph | +| 5 | `v1_frame_form_flow` | FRAME / FORM / FLOW | -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 `` placeholders; the ordered `images` array gives their paths relative to `trajectories/`, and all 357 figures are in [`trajectories/figures/`](https://huggingface.co/sensenova/SenseNova-6.8-Code2Video/tree/main/trajectories/figures). Private paths and hosts are anonymized, so recorded commands are not directly replayable. +Each line contains `messages`, `tools`, `images`, `enable_thinking` and `metadata`. The messages are kept exactly as recorded: user, assistant and tool turns, reasoning, tool calls and results, retries, context-compaction summaries and follow-up edits. Image inputs appear as `` placeholders; the `images` list gives their paths from the original runs, and the image files themselves are not included. ## License Released under the Apache License 2.0. - -## Contributors - -We thank the following contributors for their work on SenseNova 6.8 Code2Video: - -- [moolean0](https://huggingface.co/moolean0) -- [sysssyssy](https://huggingface.co/sysssyssy) -- [TTTTTony](https://huggingface.co/TTTTTony) -- [craigwu](https://huggingface.co/craigwu) -- [dder4567](https://huggingface.co/dder4567) -- [M1M1cooL](https://huggingface.co/M1M1cooL) -- [egugu](https://huggingface.co/egugu) -- [luotto](https://huggingface.co/luotto) diff --git a/trajectories/all.openai.jsonl b/trajectories/all.openai.jsonl index 66bde2fbe93a4e2126193a4b325718123c503c10..d464caa6102f651a938b16907a7432e142c12769 100644 --- a/trajectories/all.openai.jsonl +++ b/trajectories/all.openai.jsonl @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid 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