EU4-52: Discussing possible solutions

为了帮助公司节省开支,提出了推迟新数据库建设和会计软件发布的建议。通过冻结招聘并推迟某些项目,预计可节省约50万美元。

Vocabulary

[SIMON] So, where do we start?
[TODD] Well, Joan wants ideas for cutting costs.
[SIMON] Umm, The first thing I can think of is delaying some projects.
[TODD] Which ones?
[SIMON] Well, right now we're planning t
o build a new database.
[TODD] Oh, yes. That'a huge project.
[SIMON] In my opinion, we don't need it right now. We can wait six months.
[TODD] How much will that save?
[SIMON] I believe we can save half a million dollars.
[TODD] Half a million? That's fantastic.
[SIMON] Yeah. We won't have to hire developers or a project manager.
[TODD] Okay, What else?
[SIMON] We could delay the release of our new accounting software next year.
[TODD] No. We can't do that. I've already promised our best customers that it's coming in about a year.
[TODD] How many of your people are leaving in the next six months?
[SIMON] Uh, two that I know of. You're thinking of a hiring freeze, right?
[TODD] Yes, I am. It's better than layoffs.
[SIMON] Okay. I can lose one developer, but I'll still need to hire one new person for the Kingston project.
[TODD] Hmmm. That's an important project. let me think about it, okay?

Key words
  • database
  • huge
  • half a million
  • developers
  • project manager
  • release
  • software
  • customers
  • hiring freeze
  • hire

Grammar

1. Clauses with think and believe

Frank believes that he’s ready for his meeting.
Oscar thinks that Frank will make a big impression.

使用动词thinkbelieve表达观点。 注意观点在that从句,紧随动词之后。

I think that Joan did a good job.
Simon believe that we can save half a million dollars.

单词that可以省略,意思不变。

I think Joan did a good job.
Simon believes we can save half a million dollars.

回答是或者否定时用so代替that从句,避免句子重复。

A: Do you think that Joan did a good job?
B: Yes, I think so.
C: No, I don't think so.

2. Giving opinions

表达观点有许多方法,比如,使用 in my opinionit’s my feeling(that)这样的表述。

In my opinion, we could delay the software release.
It’s his feeling that we need to cut costs.

也可以使用I thinkI believe .记住,这些从句中的that可用也可不用。

I don’t believe that we should lay off staff.
He thinks a hiring freeze is better that layoffs.


3. Asking for repetition, clarification and confirmation

有时,一起讨论和表达观点时,需要重复,澄清,确认。

  • Asking for repetition

I’m sorry - could you repeat that?
I’m sorry, but I didn’t catch that.
I’m sorry - would you mind repeating that?

  • Asking for clarification

Do you mean that you think it’s good idea?
Are you saying we should stop the project?

  • Asking for confirmation

So you think he should be fired?
So, just to confirm, you believe that it’s too expensive?

有时你也可以重复别人说的话。

Half a million?


### DeepSeek-R1 32-Bit Qwen Distilled Quantized Version Details The command provided indicates the serving of a specific model variant, `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`, which suggests this is a distilled and quantized version of the original Qwen model with a focus on efficiency while maintaining performance as much as possible[^1]. For models like these, distillation involves training a smaller "student" model to mimic the behavior of a larger "teacher" model. In this case, the teacher would be an unquantized or higher precision version of Qwen. The student model (the distilled one) aims to capture most of the knowledge from its teacher but operates more efficiently due to reduced size. Quantization further enhances computational efficiency by reducing the numerical precision required for representing weights within neural networks. A 32-bit quantized model implies that each weight uses single-precision floating-point format during inference operations; however, it's worth noting that typically when discussing efficient deployment through quantization, lower bit-widths such as INT8 are often utilized because they offer significant speedups without substantial loss in accuracy compared to full-precision counterparts. Yet, specifying '32-bit' here might refer specifically to how data types were handled post-distillation rather than implying any typical low-level hardware optimization seen in other forms of quantization techniques. To deploy this particular model (`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`), parameters include setting up tensor parallelism across two GPUs using `tensor-parallel-size 2`. This configuration allows splitting large layers over multiple devices so that even very wide architectures can fit into memory constraints imposed by individual GPU capacities. Additionally, enforcing eager execution mode ensures immediate evaluation instead of building graphs first—a choice beneficial for interactive applications where responsiveness matters significantly. ```bash vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \ --tensor-parallel-size 2 \ --max-model-len 32768 \ --enforce-eager ``` This setup supports handling sequences up to length 32,768 tokens long (`--max-model-len 32768`)—a feature critical for tasks requiring context awareness over extensive text spans beyond what standard configurations usually support. --related questions-- 1. What advantages does model distillation provide for deploying AI systems? 2. How does tensor parallelism improve the scalability of large language models? 3. Can you explain why enforcing eager execution may benefit certain application scenarios? 4. Why is supporting longer sequence lengths important for some NLP tasks?
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