total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock 250 300 DeepSeek-V2 DeepSeek 67B saving 42.5% of training costs Training Costs (K GPU Hours/T Tokens) 0 100 200 300 400 DeepSeek-V2 DeepSeek 67B reducing KV cache by 93.3% KV Cache for Generation

what’s in the previous tokens and what the LLM has seen during its training. When you write a prompt, you are attempting to set up the LLM to predict the right sequence of tokens. Prompt engineering is task. Output length An important configuration setting is the number of tokens to generate in a response. Generating more tokens requires more computation from the LLM, leading to higher energy consumption or textually succinct in the output it creates, it just causes the LLM to stop predicting more tokens once the limit is reached. If your needs require a short output length, you’ll also possibly need

Note: In AI language models, tokens represent basic units of text (e.g., words or sub-words) used during training. Training dataset sizes are often measured in total tokens processed. A larger token count Source: Epoch AI (5/25) AI Model Training Dataset Size (Tokens) by Model Release Year – 6/10-5/25, per Epoch AI Training Dataset Size, Tokens CapEx Spend – Big Technology Companies = Inflected With Number of GPUs 46K 43K 28K 16K 11K +225x Factory AI FLOPS 1EF 5EF 17EF 63EF 220EF Annual Inference Tokens 50B 1T 5T 58T 1,375T +30,000x Annual Token Revenue $240K $3M $24M $300M $7B DC Power 37MW 34MW

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 12.1. Passwords and JWT (JSON Web Tokens) usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 12.1 documentation de ce projet 36 11. Deployment 37 12. Security 12.1. Passwords and JWT (JSON Web Tokens) usage 12.1.1. How are set and used passwords and JWT in Apache OFBiz The Apache OFBiz Project As says Wikipedia: JSON Web Token (JWT) is an Internet standard for creating JSON-based access tokens that assert some number of claims. We currently use JWT in 2 places: 1. To let users safely recreate

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 12.2. Passwords and JWT (JSON Web Tokens) usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 12.2 to follow https://issues.apache.org/jira/ browse/OFBIZ-10213 12.2. Passwords and JWT (JSON Web Tokens) usage 12.2.1. How are set and used passwords and JWT in Apache OFBiz The Apache OFBiz Project As says Wikipedia: JSON Web Token (JWT) is an Internet standard for creating JSON-based access tokens that assert some number of claims. 65 We currently use JWT in 2 places: 1. To let users safely

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 12.2. Passwords and JWT (JSON Web Tokens) usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 12.2 to follow https://issues.apache.org/jira/ browse/OFBIZ-10213 12.2. Passwords and JWT (JSON Web Tokens) usage 12.2.1. How are set and used passwords and JWT in Apache OFBiz The Apache OFBiz Project As says Wikipedia: JSON Web Token (JWT) is an Internet standard for creating JSON-based access tokens that assert some number of claims. 92 We currently use JWT in 2 places: 1. To let users safely

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 12.2. Passwords and JWT (JSON Web Tokens) usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 12.2 to follow https://issues.apache.org/jira/ browse/OFBIZ-10213 12.2. Passwords and JWT (JSON Web Tokens) usage 12.2.1. How are set and used passwords and JWT in Apache OFBiz The Apache OFBiz Project As says Wikipedia: JSON Web Token (JWT) is an Internet standard for creating JSON-based access tokens that assert some number of claims. 92 We currently use JWT in 2 places: 1. To let users safely

其在 ⼤模型⽀持的上下⽂窗⼝⽅⾯。 上下⽂窗⼝有多重要？回想⼀下使⽤
ChatGPT
处理⻓⽂的经历，你是不是也经常收到「⽂本过⻓」的 提⽰？这是因为
ChatGPT
⾄多只⽀持
32k
tokens（约
2.5
万汉字）的上下⽂。这⼀限制让很多⾏业的 从业⼈员都很头疼，尤其是律师、分析师、咨询师、prompt
⼯程师等需要分析、处理较⻓⽂本的⼯ 作。

此外，
token
数量 重模型「幻觉」，就像
Character.AI
等聊天
AI
产品的⽤⼾所抱怨的那样。


为了解决这些问题，Anthropic
在其产品
Claude
2.0
中提供了
100k
tokens（实测约
8
万汉字）的上 下⽂窗⼝，⼤⼤扩展了其产品的应⽤空间。 那么，如果这个窗⼝再⼤⼀点呢？ 这是国内⼤模型初创公司
Moonshot
AI
推出的⼤模型智能助⼿
Kimi
智能助⼿，拥有超⻓的上下⽂窗 ⾄能⼀次处理
20
万字，⼤约是
Claude
2.0
中⽂上下⽂窗⼝的
2.5
倍，是全球市场上能够产品化使⽤的 ⼤模型服务中所能⽀持的最⻓上下⽂输⼊⻓度。GPT-4
等英⽂为基础的模型产品需要做到接近
250k
tokens
才能获得与
Kimi
智能助⼿
同等的汉字上下⽂窗⼝。
那么，这么⼤的上下⽂窗⼝⽤起来是⼀种怎样的体验？Moonshot
AI
是怎么做到这⼀点的？在该功能 开放内测之际，机器之⼼在第⼀

Apache OFBiz and how to adapt it if needed . . . . . . . . 29 11.2. Passwords and JWT (JSON Web Tokens) usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 11.2 allows to change to 'lax' if needed. SameSiteCookieAttribute= 11.2. Passwords and JWT (JSON Web Tokens) usage 11.2.1. How are set and used passwords and JWT in Apache OFBiz The Apache OFBiz Project As says Wikipedia: JSON Web Token (JWT) is an Internet standard for creating JSON-based access tokens that assert some number of claims. We currently use JWT in 2 places: 1. To let users safely recreate

lookups are available in the Management interface: is:bot Search for bots (used for project scoped tokens). is:active Search for active users. email:TEXT Search by e-mail. Translation workflows Using org/rfc/rfc6585#sectio 4] – when throttling is in place Authentication tokens Changed in version 4.10: Project scoped tokens were introduced in the 4.10 release. Each user has his personal access token the user profile. Newly generated user tokens have the wlu_ prefix. It is possible to create project scoped tokens for API access to given project only. These tokens can be identified by the wlp_ prefix