{"schema_version":"onlylabs.public_signal.v1","title":"Tencent Hunyuan Model: tencent/Sequential-Hidden-Decoding-8B-n2","description":"Tencent Hunyuan model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/2b717243-58e0-4e2d-a5b9-2a7d8b9d4ec4","json_url":"https://onlylabs.fyi/signals/2b717243-58e0-4e2d-a5b9-2a7d8b9d4ec4/signal.json","generated_at":"2026-06-11T02:49:40.894922+00:00","org":{"slug":"tencent-hunyuan","name":"Tencent Hunyuan","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/tencent-hunyuan","dossier_json_url":"https://onlylabs.fyi/labs/tencent-hunyuan/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/2b717243-58e0-4e2d-a5b9-2a7d8b9d4ec4","signal_json":"https://onlylabs.fyi/signals/2b717243-58e0-4e2d-a5b9-2a7d8b9d4ec4/signal.json","source":"https://huggingface.co/tencent/Sequential-Hidden-Decoding-8B-n2","lab_dossier":"https://onlylabs.fyi/labs/tencent-hunyuan","lab_dossier_json":"https://onlylabs.fyi/labs/tencent-hunyuan/dossier.json","analysis":"https://onlylabs.fyi/analysis/tencent-hunyuan","analysis_json":"https://onlylabs.fyi/analysis/tencent-hunyuan/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/tencent-hunyuan/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"Tencent Hunyuan published tencent/Sequential-Hidden-Decoding-8B-n2. 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It is intended for benchmarking, text completion, and as a foundation for downstream fine-tuning (SFT / RLHF). For conversational or instruction-following use cases, please fine-tune on your own data. Key Idea Prepare *n* independent Embedding matrices to encode the same token sequence *n* times, interleave the results, and feed the *n*×-length sequence into the same Transformer. Only the last embedding of each token computes the next-token loss, while the preceding embeddings serve as implicit reasoning steps in a continuous latent space. Results..."},"evidence_pages":[{"url":"https://huggingface.co/tencent/Sequential-Hidden-Decoding-8B-n2/raw/main/README.md","final_url":"https://huggingface.co/tencent/Sequential-Hidden-Decoding-8B-n2/raw/main/README.md","title":"tencent/Sequential-Hidden-Decoding-8B-n2 model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:40.894922+00:00","bytes":3593,"raw_path":"f1b261cb1654ae71b297173862611bee5629f90886098ade902f7edf1a44b22b.md","content_hash":"e6c7583960e659df0de4eaa0c06a7bf71f27989c1ff7c5515ab6ad4d69af9035","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: other license_name: sequential-hidden-decoding license_link: LICENSE base_model: - Qwen/Qwen3-8B-Base tags: - sequential-hidden-decoding - pretrained - base-model --- Sequential-Hidden-Decoding-8B-n2 This is the **n=2** variant of Sequential Hidden Decoding, a method that scales sequence length by n× with only additional Embedding parameters — same Transformer, more compute per token. - **Base model:** [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) - **Scale:** 2× - **Additional Embedding Params:** 1.9B - **Training Tokens:** 75B - **Dtype:** bfloat16 > **Note:** This is a **base model** (not instruction-tuned). It is intended for benchmarking, text completion, and as a foundation for downstream fine-tuning (SFT / RLHF). For conversational or instruction-following use cases, please fine-tune on your own data. Key Idea Prepare *n* independent Embedding matrices to encode the same token sequence *n* times, interleave the results, and feed the *n*×-length sequence into the same Transformer. Only the last embedding of each token computes the next-token loss, while the preceding embeddings serve as implicit reasoning steps in a continuous latent space. 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