Alex Getmanvroege AI-signalen

DeepSeek R1

DeepSeek

Uitgebracht Bijgewerkt
Contextvenster
64K
64.000 tokens

Tijdlijn

  1. · Gewichtsversie · bronlink
  1. · Bronlinks · GitHub search · DeepSeek · commits · warejohn4366/war · @warejohn4366
    Fragment
    随着AI发展,企业和开发者希望快速应用AI模型。本文介绍在阿里云PAI Model Gallery上零代码一键部署DeepSeek-V3和DeepSeek-R1模型的方法,包括准备工作、部署步骤、调用方式等。这种方式简化流程、提高效率、降低成本,助力AI能力落地。
  2. · Bronlinks · GitHub search · DeepSeek · pulls · overnightworks/speech · @dependabot[bot]
    Fragment
    XTX, <a href="https://redirect.github.com/vllm-project/vllm/issues/59774">#59774</a>); and gfx1250 enablement for gpt-oss-120b, DeepSeek-R1, DeepSeek-V4-Flash and MiniMax-M3 (<a href="https://redirect.github.com/vllm-project/vllm/issues/58800">#58800</a>).</li>
  3. · Bronlinks · GitHub search · DeepSeek · pulls · anomalyco/models.dev · @models-dev-automation[bot]
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    Instruct-Turbo`, `openai/gpt-oss-120b-Turbo`, `google/gemma-4-31B-it-Ultra`, `mattshumer/Reflection-Llama-3.1-70B`, `NovaSky-AI/Sky-T1-32B-Preview`, `deepseek-ai/DeepSeek-R1`, `Qwen/Qwen2-7B-Instruct`, `Qwen/QwQ-32B`, `google/gemma-2-9b-it`, `google/gemma-4-31B-it-turbo`, `Qwen/Qwen3.5-0.8B`, `deepseek-ai/DeepSeek-V3-0324-Turbo`, `openbmb/MiniCPM-Llama3-V-2_5`, `microsoft/phi-4`, `inclusionAI/Ling-3.0-flash-VL`, `Qwen/Qwen3-VL-30B-A3B-Thinking`, `MiniMaxAI/MiniMax-M2.7-Turbo`, `deepseek-ai
  4. · Bronlinks · GitHub search · DeepSeek · pulls · ROCm/aiter · @karverma-amd
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    Tuned configs for DeepSeek-R1 MLA-absorb bmms (B=128). Previously fell back to DEFAULT (untuned): 5.5x slower at decode M~1536 . Lets the BF16 MLA-absorb path run
  5. · Bronlinks · GitHub search · DeepSeek · pulls · NVIDIA/TensorRT-LLM · @moraxu
  6. · Bronlinks · GitHub search · DeepSeek · pulls · NVIDIA/TensorRT-LLM · @farazkh80
    Fragment
    with all of its KV remote and MLA (DeepSeek-R1 shape) 1.43x, which is why allocation and placement keep pages local. Partitioned attention rejects FlashMLA, beam
  7. · Catalogusvermelding · GitHub · openai/openai-agents-python · code
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    object="chat.completion.chunk", choices=[Choice(index=0, delta=delta)], ) for delta in [*reasoning_deltas, ChoiceDelta(content="answer")] ] events = await _collect_handler_events(*chunks, model="deepseek-r1") completed_event = next(event for event in events if event.type == "response.completed") reasoning_item = completed_event.response.output[0] assert isinstance(reasoning_item, ResponseReasoningItem) assert reasoning_item.summary[0].text == "summary" assert reasoning_item.content assert [part.text for part in reasoning_item.content] == ["raw"] assert "_chat_completions_reasoning_field" not in cast(Any, reasoning_item).provider_data replayed_messages = Converter.items_to_messages( [item.model_dump() for item in completed_event.response.output], model="deepseek-r1", ) assert len

    Deze codeverwijzing bevestigt geen modelidentificatie die daadwerkelijk wordt gebruikt.

Signalen 1

Specificaties

DeepSeek R1 is een AI-model van DeepSeek met open gewichten, uitgebracht op 2025-01-20. Het contextvenster is 64.000 tokens.

Contextvenster
64.000
Gewichten
Open

Vragen

Wat is het contextvenster van DeepSeek R1?

Het contextvenster is 64.000 tokens.

Zijn de gewichten van DeepSeek R1 openbaar beschikbaar?

Ja. DeepSeek publiceert de modelgewichten.

Wanneer werd DeepSeek R1 uitgebracht?

2025-01-20.