गाइड से जुड़ी प्रश्नोत्तरी · मध्यम स्तर
डीपसीक क्विज़
वर्तमान संस्करण विवरण के साथ DeepSeek R1 की दिनांकित रिलीज़, मॉडल-विशिष्ट लाइसेंसिंग और नए V4.1-फ़्लैश एपीआई की समीक्षा करें।
प्रश्न 1 का 8
Which reasoning model did DeepSeek announce on January 20, 2025?
प्रत्येक प्रश्न का पूर्वावलोकन करें8 प्रश्न
जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।
- Which reasoning model did DeepSeek announce on January 20, 2025?
- DeepSeek-R1
- DeepSeek-V4.1-Flash
- DeepSeek-V3.2
- DeepSeek-Coder
- What architecture and active-parameter counts does DeepSeek report for V4.1-Flash?
- A 70B dense model with all parameters active at every stage
- A 552B mixture-of-experts model with 8B active for input and 16B for output
- A 7B image-only convolutional network
- A 1B recurrent model with no expert routing
- Which organization published the official DeepSeek-R1 model release?
- High-Flyer Research only
- OpenAI
- DeepSeek-AI
- Google डीपमाइंड
- How did R1-Zero differ from DeepSeek-R1’s training recipe?
- R1-Zero was image-only, while R1 was text-only
- R1-Zero used only human preference labels, while R1 had no training
- They used identical training stages
- R1-Zero used large-scale RL without an initial SFT step, while R1 added cold-start data and multi-stage training
- Which reward signals does the DeepSeek-R1 paper describe for its reasoning-oriented reinforcement-learning stage?
- Rule-based accuracy rewards and format rewards
- Only human preference labels from a reward model
- Image-caption alignment rewards
- A reward based only on response length
- What license did DeepSeek state for the DeepSeek-R1 code and model weights?
- No license was provided
- MIT, for that specific release
- A blanket license identical for every DeepSeek release
- A research-only license
- Which context window does the official DeepSeek model list report for V4.1-Flash?
- 8,192 tokens
- 32,768 tokens
- 1,048,576 tokens
- 128,000 टोकन
- What modality did DeepSeek announce for V4.1-Flash alongside text generation?
- Only MIDI output
- Radar-only sensor input
- No input beyond plain text
- Native image input