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1python -m sglang.launch_server \
2 --model-path tencent/Sequential-Hidden-Decoding-8B-n8-Instruct \
3 --trust-remote-code \
4 --tp-size 1 \
5 --port 30000 --host 0.0.0.0 \
6 --chunked-prefill-size -1 \
7 --attention-backend fa3 \
8 --mem-fraction-static 0.82 \
9 --max-running-requests 32 \
10 --context-length 131072 \
11 --cuda-graph-max-bs 128 \
12 --cuda-graph-bs 1 2 4 8 16 32 64 128Note: Sequential Hidden Decoding models process n×-length sequences internally, so--chunked-prefill-size -1,--attention-backend fa3, and conservative batch sizing are important for stability and performance.
/v1/chat/completions endpoint:1from openai import OpenAI
2
3client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
4response = client.chat.completions.create(
5 model="tencent/Sequential-Hidden-Decoding-8B-n8-Instruct",
6 messages=[
7 {"role": "system", "content": "You are a helpful assistant."},
8 {"role": "user", "content": "Explain the idea of hidden decoding in simple terms."},
9 ],
10 max_tokens=512,
11 temperature=0.7,
12)
13print(response.choices[0].message.content)trust_remote_code:configuration_qwen3_scale_seq.pymodeling_qwen3_scale_seq.py| Model | Type | Notes |
|---|---|---|
| Sequential-Hidden-Decoding-8B-n2 | Base | 2x scale base model |
| Sequential-Hidden-Decoding-8B-n4 | Base | 4x scale base model |
| Sequential-Hidden-Decoding-8B-n8 | Base | 8x scale base model |
| Sequential-Hidden-Decoding-8B-n8-Instruct | Instruct | Instruction-tuned 8x scale model |
1@article{hidden_decoding_2026,
2 title = {Hidden Decoding: Scaling Sequence Length in Pretraining},
3 year = {2026},
4 url = {https://welm.weixin.qq.com/posts/hidden_decoding/}
5}