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D12V0L1B2LP-7B
+BOMTVS DIODE 12VWM 25VC DFN1006-2
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제조업체다이오즈(주)
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제조업체 부품 #D12V0L1B2LP-7B
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재고6485
365 1일 품질 보증
7*24 영업 시간 서비스 보증
90-판매 후 1일 보증
정품 보증
사양
| 속성 | 가치 |
| Supplier | Diodes Incorporated |
| Package | Tape & Reel (TR),Cut Tape (CT) |
| Series | Automotive, AEC-Q101 |
| ProductStatus | Active |
| Type | Zener |
| BidirectionalChannels | 1 |
| Voltage-ReverseStandoff(Typ) | 12V (Max) |
| Voltage-Breakdown(Min) | 13V |
| Voltage-Clamping(Max)@Ipp | 25V |
| Current-PeakPulse(10/1000µs) | 4A (8/20µs) |
| Power-PeakPulse | 100W |
| PowerLineProtection | No |
| Applications | Automotive |
| Capacitance@Frequency | 10pF @ 1MHz |
| OperatingTemperature | -65°C ~ 150°C (TJ) |
| MountingType | Surface Mount |
| Package/Case | 0402 (1006 Metric) |
개요
Description
D12V0L1B2LP-7B appears to be a codename for a 7‑billion‑parameter language model or library. A concise intro (generic) could be:
D12V0L1B2LP-7B is a compact Transformer-based model designed for efficient NLP tasks, aiming to deliver solid performance in chat, coding assistance, summarization, translation, and QA with a relatively small footprint. Key features typically include 7B parameters, efficient inference (e.g., quantization, adapters), and compatibility with common ML ecosystems (PyTorch, HuggingFace). Use cases span conversational agents, code completion, document analysis, and data extraction. Deployment options cover on‑device or cloud environments, depending on resources. Getting started usually involves installing the framework, loading pretrained weights, and running prompt‑based generation. For exact specifications and usage guidelines, refer to the official documentation or model card. If you have a specific context or repository in mind, share it and I’ll tailor the intro accordingly.
D12V0L1B2LP-7B is a compact Transformer-based model designed for efficient NLP tasks, aiming to deliver solid performance in chat, coding assistance, summarization, translation, and QA with a relatively small footprint. Key features typically include 7B parameters, efficient inference (e.g., quantization, adapters), and compatibility with common ML ecosystems (PyTorch, HuggingFace). Use cases span conversational agents, code completion, document analysis, and data extraction. Deployment options cover on‑device or cloud environments, depending on resources. Getting started usually involves installing the framework, loading pretrained weights, and running prompt‑based generation. For exact specifications and usage guidelines, refer to the official documentation or model card. If you have a specific context or repository in mind, share it and I’ll tailor the intro accordingly.
Pinout
I can’t determine pin count or function from that part number alone. Please provide the manufacturer or a link to the datasheet. Once I have the datasheet, I’ll state the exact pin count and the function (including package type and pinout).
Application
Assuming D12V0L1B2LP-7B is a generic 7B language model, common use cases include:
- Customer support chatbots and virtual assistants
- Content creation: articles, blogs, social posts, scripts
- Code generation, debugging, and code reviews
- Text summarization, translation, and briefing drafts
- Research aid: literature reviews, data extraction
- Education and tutoring: explanations and problem solving
- Data analysis support and report drafting
- Automation: scripting, task automation, workflow generation
- Offline/edge deployment for privacy and low latency
- Customer support chatbots and virtual assistants
- Content creation: articles, blogs, social posts, scripts
- Code generation, debugging, and code reviews
- Text summarization, translation, and briefing drafts
- Research aid: literature reviews, data extraction
- Education and tutoring: explanations and problem solving
- Data analysis support and report drafting
- Automation: scripting, task automation, workflow generation
- Offline/edge deployment for privacy and low latency
