GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
sensenova
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
sensenova
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| 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 |
bash
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 8000bash
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 8000bash
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 8000A 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 |
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:
json
{ "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.
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 |
Stage 2 with the settings above:
bash
cd /path/to/task # contains dense_prompt.md, assets/ and .claude/skills/code-videoclaude -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.
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.
Released under the Apache License 2.0.
We thank the following contributors for their work on SenseNova 6.8 Code2Video:
| Time per run | 10 h+ | timeout 10h or more |
| A new version of the film |