PANDUAN Syarikat

DeepSeek

DeepSeek is an AI model developer whose releases include DeepSeek-R1 and later versioned model families.

Dengar panduan iniAI-diceritakan · ~2 min

Pada halaman ini2 min dibaca
  1. Gambaran keseluruhan
  2. Menyelam dalam
  3. Pelaksanaan Dunia Sebenar
  4. Masa Depan DeepSeek
  5. Mempraktikkannya
  6. Teruskan Meneroka
  7. Soalan lazim

Gambaran keseluruhan

Technical claims, benchmark results, licenses and API features must be tied to the exact release.

Menyelam dalam

The DeepSeek-R1 paper describes an RL-oriented reasoning approach; R1-Zero uses large-scale reinforcement learning without an initial supervised-fine-tuning stage, while R1 adds cold-start data and additional stages. The official R1 repository states MIT terms for that release’s code and model weights. In September 2026, DeepSeek announced V4.1-Flash as a 552B mixture-of-experts model with 8B active parameters for input and 16B for output, plus native visual understanding. Official API model metadata lists a 1,048,576-token context window for V4.1-Flash. These details belong to specific releases and should not be generalized across the model family.

Pelaksanaan Dunia Sebenar

Compare the R1 and R1-Zero training descriptions in the dated technical report.

Check the official R1 repository for the license stated for that release.

For V4.1-Flash, verify the architecture and active-parameter counts against its September 2026 release post.

Before integrating an API model, confirm the current model identifier and supported modalities in the official model list.

Masa Depan DeepSeek

DeepSeek model names, routing and API specifications can change. Check current official release posts and model metadata before using a particular version. Treat company-reported benchmark comparisons as scoped to their stated tasks and conditions; do not infer that one model is universally cheapest or best.

Mempraktikkannya

Mengapa ia penting

  • Strategi vendor. Peta jalan vendor mempengaruhi ciri yang boleh dibina oleh pasukan anda seterusnya.
  • Kos dan bajet. Terma komersial dan pilihan penggunaan mempengaruhi kos dan risiko jangka panjang.
  • Risiko dan keselamatan. Insentif syarikat membentuk keingkaran produk, postur keselamatan dan keterbukaan.

Berhati-hati untuk

  • Pengumuman pelancaran mungkin melebihi kestabilan dalam aliran kerja pengeluaran sebenar.
  • Harga API atau anjakan dasar boleh memecahkan andaian semalaman.
  • Kebergantungan vendor tunggal meningkatkan kos kunci masuk dan penghijrahan.

Langkah seterusnya

  1. Nilai penyedia menggunakan tugasan dan set data anda sendiri.
  2. Semak privasi, keselamatan dan syarat undang-undang sebelum penyepaduan.
  3. Kekalkan pelan sandaran merentas model atau vendor.
  4. Pantau nota keluaran supaya perubahan peta jalan tidak mengejutkan pasukan.

Teruskan Meneroka

Newsletter percuma

Dapatkan taklimat AI harian

Tiga cerita AI yang disahkan setiap pagi hari bekerja, ditulis dalam Bahasa Inggeris biasa. Percuma selamanya, tiada iklan.

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Uji diri anda

Take the DeepSeek quiz

Maklum balas segera pada setiap jawapan, dan sijil yang boleh dikongsi dengan ID yang boleh disahkan sebaik sahaja anda lulus kursus.

Mulakan kuiz

Sokong pendidikan AI percuma. AI Understanding ialah pertubuhan bukan keuntungan 501(c)(3) — tiada iklan, tiada bayaran dinding, selamanya. Buat derma

Soalan lazim

What is DeepSeek?

DeepSeek is an AI model developer whose releases include DeepSeek-R1 and later versioned model families. Technical claims, benchmark results, licenses and API features must be tied to the exact release.

Which reasoning model did DeepSeek announce on January 20, 2025?

DeepSeek’s dated release announcement introduced DeepSeek-R1 on January 20, 2025; this identifies the model without making a comparative price or performance claim.

What architecture and active-parameter counts does DeepSeek report for V4.1-Flash?

DeepSeek’s September 10, 2026 release announcement describes V4.1-Flash as a 552B MoE model with 8B active parameters for input and 16B for output. These details apply to this version only.

Which organization published the official DeepSeek-R1 model release?

The R1 paper and official repository are published by DeepSeek-AI; this is directly supported by the release rather than an inferred corporate-origin claim.

How did R1-Zero differ from DeepSeek-R1’s training recipe?

The primary report says R1-Zero used large-scale RL without supervised fine-tuning as a preliminary step, whereas R1 adds cold-start data and a multi-stage process.

Which reward signals does the DeepSeek-R1 paper describe for its reasoning-oriented reinforcement-learning stage?

The R1 technical report describes rule-based rewards for verifiable accuracy and format adherence in its reinforcement-learning stages; it does not establish that every DeepSeek model uses the same recipe.