mirror of
https://github.com/privacyguides/i18n.git
synced 2025-09-01 02:49:14 +00:00
New Crowdin translations by GitHub Action
This commit is contained in:
Crowdin Bot
@@ -12,7 +12,7 @@ cover: ai-chatbots.webp
|
||||
- [:material-account-cash: Surveillance Capitalism](basics/common-threats.md#surveillance-as-a-business-model){ .pg-brown }
|
||||
- [:material-close-outline: Censorship](basics/common-threats.md#avoiding-censorship){ .pg-blue-gray }
|
||||
|
||||
Since the release of ChatGPT in 2022, interactions with Large Language Models (LLMs) have become increasingly common. LLMs can help us write better, understand unfamiliar subjects, or answer a wide range of questions. They can statistically predict the next word based on a vast amount of data scraped from the web.
|
||||
The use of **AI chat**, also known as Large Language Models (LLMs), has become increasingly common since the release of ChatGPT in 2022. LLMs can help us write better, understand unfamiliar subjects, or answer a wide range of questions. They work by statistically predicting the next word in their responses based on a vast amount of data scraped from the web.
|
||||
|
||||
## Privacy Concerns About LLMs
|
||||
|
||||
@@ -42,7 +42,7 @@ To run AI locally, you need both an AI model and an AI client.
|
||||
|
||||
### Choosing a Model
|
||||
|
||||
There are many permissively licensed models available to download. [Hugging Face](https://huggingface.co/models) is a platform that lets you browse, research, and download models in common formats like [GGUF](https://huggingface.co/docs/hub/en/gguf). Companies that provide good open-weights models include big names like Mistral, Meta, Microsoft, and Google. However, there are also many community models and 'fine-tunes' available. As mentioned above, quantized models offer the best balance between model quality and performance for those using consumer-grade hardware.
|
||||
There are many permissively licensed models available to download. [Hugging Face](https://huggingface.co/models) is a platform that lets you browse, research, and download models in common formats like [GGUF](https://huggingface.co/docs/hub/en/gguf). Companies that provide good open-weights models include big names like Mistral, Meta, Microsoft, and Google. However, there are also many community models and [fine-tuned](https://en.wikipedia.org/wiki/Fine-tuning_\(deep_learning\)) models available. As mentioned above, quantized models offer the best balance between model quality and performance for those using consumer-grade hardware.
|
||||
|
||||
To help you choose a model that fits your needs, you can look at leaderboards and benchmarks. The most widely-used leaderboard is the community-driven [LM Arena](https://lmarena.ai). Additionally, the [OpenLLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) focuses on the performance of open-weights models on common benchmarks like [MMLU-Pro](https://arxiv.org/abs/2406.01574). There are also specialized benchmarks which measure factors like [emotional intelligence](https://eqbench.com), ["uncensored general intelligence"](https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard), and [many others](https://www.nebuly.com/blog/llm-leaderboards).
|
||||
|
||||
@@ -63,7 +63,7 @@ To help you choose a model that fits your needs, you can look at leaderboards an
|
||||
|
||||
{align=right}
|
||||
|
||||
Kobold.cpp is an AI client that runs locally on your Windows, Mac, or Linux computer. It's an excellent choice if you are looking for heavy customization and tweaking, such as for role-playing purposes.
|
||||
**Kobold.cpp** is an AI client that runs locally on your Windows, Mac, or Linux computer. It's an excellent choice if you are looking for heavy customization and tweaking, such as for role-playing purposes.
|
||||
|
||||
In addition to supporting a large range of text models, Kobold.cpp also supports image generators such as [Stable Diffusion](https://stability.ai/stable-image) and automatic speech recognition tools such as [Whisper](https://github.com/ggerganov/whisper.cpp).
|
||||
|
||||
@@ -83,7 +83,7 @@ In addition to supporting a large range of text models, Kobold.cpp also supports
|
||||
|
||||
</div>
|
||||
|
||||
<div class="admonition note" markdown>
|
||||
<div class="admonition info" markdown>
|
||||
<p class="admonition-title">Compatibility Issues</p>
|
||||
|
||||
Kobold.cpp might not run on computers without AVX/AVX2 support.
|
||||
@@ -98,7 +98,7 @@ Kobold.cpp allows you to modify parameters such as the AI model temperature and
|
||||
|
||||
{align=right}
|
||||
|
||||
Ollama is a command-line AI assistant that is available on macOS, Linux, and Windows. Ollama is a great choice if you're looking for an AI client that's easy-to-use, widely compatible, and fast due to its use of inference and other techniques. It also doesn't involve any manual setup.
|
||||
**Ollama** is a command-line AI assistant that is available on macOS, Linux, and Windows. Ollama is a great choice if you're looking for an AI client that's easy-to-use, widely compatible, and fast due to its use of inference and other techniques. It also doesn't involve any manual setup.
|
||||
|
||||
In addition to supporting a wide range of text models, Ollama also supports [LLaVA](https://github.com/haotian-liu/LLaVA) models and has experimental support for Meta's [Llama vision capabilities](https://huggingface.co/blog/llama32#what-is-llama-32-vision).
|
||||
|
||||
@@ -124,9 +124,9 @@ Ollama simplifies the process of setting up a local AI chat by downloading the A
|
||||
|
||||
<div class="admonition recommendation" markdown>
|
||||
|
||||
{align=right}
|
||||
{align=right}
|
||||
|
||||
Llamafile is a lightweight single-file executable that allows users to run LLMs locally on their own computers without any setup involved. It is [backed by Mozilla](https://hacks.mozilla.org/2023/11/introducing-llamafile) and available on Linux, macOS, and Windows.
|
||||
**Llamafile** is a lightweight, single-file executable that allows users to run LLMs locally on their own computers without any setup involved. It is [backed by Mozilla](https://hacks.mozilla.org/2023/11/introducing-llamafile) and available on Linux, macOS, and Windows.
|
||||
|
||||
Llamafile also supports LLaVA. However, it doesn't support speech recognition or image generation.
|
||||
|
||||
@@ -138,7 +138,9 @@ Llamafile also supports LLaVA. However, it doesn't support speech recognition or
|
||||
<details class="downloads" markdown>
|
||||
<summary>Downloads</summary>
|
||||
|
||||
- [:fontawesome-solid-desktop: Desktop](https://github.com/Mozilla-Ocho/llamafile#quickstart)
|
||||
- [:fontawesome-brands-windows: Windows](https://github.com/Mozilla-Ocho/llamafile#quickstart)
|
||||
- [:simple-apple: macOS](https://github.com/Mozilla-Ocho/llamafile#quickstart)
|
||||
- [:simple-linux: Linux](https://github.com/Mozilla-Ocho/llamafile#quickstart)
|
||||
|
||||
</details>
|
||||
|
||||
@@ -161,7 +163,7 @@ To check the authenticity and safety of the model, look for:
|
||||
- Community reviews and usage statistics
|
||||
- A "Safe" badge next to the model file (Hugging Face only)
|
||||
- Matching checksums[^1]
|
||||
- On Hugging Face, you can find the hash by clicking on a model file and looking for the **Copy SHA256** button below it. You should compare this checksum with the one from the model file you downloaded.
|
||||
- On Hugging Face, you can find the hash by clicking on a model file and looking for the **Copy SHA256** button below it. You should compare this checksum with the one from the model file you downloaded.
|
||||
|
||||
A downloaded model is generally safe if it satisfies all the above checks.
|
||||
|
||||
@@ -171,11 +173,11 @@ Please note we are not affiliated with any of the projects we recommend. In addi
|
||||
|
||||
### Minimum Requirements
|
||||
|
||||
- Must be open-source.
|
||||
- Must be open source.
|
||||
- Must not transmit personal data, including chat data.
|
||||
- Must be multi-platform.
|
||||
- Must not require a GPU.
|
||||
- Must support GPU-powered fast inference.
|
||||
- Must support GPU-powered, fast inference.
|
||||
- Must not require an internet connection.
|
||||
|
||||
### Best-Case
|
||||
@@ -186,4 +188,11 @@ Our best-case criteria represent what we _would_ like to see from the perfect pr
|
||||
- Should have a built-in model downloader option.
|
||||
- The user should be able to modify the LLM parameters, such as its system prompt or temperature.
|
||||
|
||||
\*[LLaVA]: Large Language and Vision Assistant (multimodal AI model)
|
||||
\*[LLM]: Large Language Model (AI model such as ChatGPT)
|
||||
\*[LLMs]: Large Language Models (AI models such as ChatGPT)
|
||||
\*[open-weights models]: AI models that anyone can download and use, but the underlying training data and/or algorithms for them are proprietary.
|
||||
\*[system prompt]: The general instructions given by a human to guide how an AI chat should operate.
|
||||
\*[temperature]: A parameter used in AI models to control the level of randomness and creativity in the generated text.
|
||||
|
||||
[^1]: A file checksum is a type of anti-tampering fingerprint. A developer usually provides a checksum in a text file that can be downloaded separately, or on the download page itself. Verifying that the checksum of the file you downloaded matches the one provided by the developer helps ensure that the file is genuine and wasn't tampered with in transit. You can use commands like `sha256sum` on Linux and macOS, or `certutil -hashfile file SHA256` on Windows to generate the downloaded file's checksum.
|
||||
|
||||
@@ -19,7 +19,7 @@ description: 电子邮件在许多方面本身就不安全，以下是它不是
|
||||
|
||||
## 什么是网络密钥目录标准？
|
||||
|
||||
网络密钥目录 (WKD) 标准允许电子邮件客户端发现其他邮箱的 OpenPGP 密钥，即使是托管在不同提供商的邮箱。 支持 WKD 的电子邮件客户端会要求收件人的服务器根据电子邮件地址的域名提供密钥。 例如，如果您向 `jonah@privacyguides.org`发送电子邮件，您的电子邮件客户端会向 `privacyguides.org` 询问 Jonah 的 OpenPGP 密钥，如果 `privacyguides.org` 拥有该账户的密钥，您的邮件就会自动加密。
|
||||
The [Web Key Directory (WKD)](https://wiki.gnupg.org/WKD) standard allows email clients to discover the OpenPGP key for other mailboxes, even those hosted on a different provider. 支持 WKD 的电子邮件客户端会要求收件人的服务器根据电子邮件地址的域名提供密钥。 例如，如果您向 `jonah@privacyguides.org`发送电子邮件，您的电子邮件客户端会向 `privacyguides.org` 询问 Jonah 的 OpenPGP 密钥，如果 `privacyguides.org` 拥有该账户的密钥，您的邮件就会自动加密。
|
||||
|
||||
除了我们推荐的 [电子邮件客户端（](../email-clients.md) ）支持 WKD 外，一些网络邮件提供商也支持 WKD。 *您自己的* 密钥是否发布到 WKD 供他人使用，取决于您的域配置。 如果您使用支持 WKD 的 [电子邮件提供商](../email.md#openpgp-compatible-services) （如 Proton Mail 或 Mailbox.org），他们可以为您在其域上发布 OpenPGP 密钥。
|
||||
|
||||
|
||||
@@ -34,7 +34,7 @@ In addition to (or instead of) an email provider recommended here, you may wish
|
||||
|
||||
## OpenPGP 兼容服务
|
||||
|
||||
These providers natively support OpenPGP encryption/decryption and the [Web Key Directory standard](basics/email-security.md#what-is-the-web-key-directory-standard), allowing for provider-agnostic E2EE emails. 例如，Proton Mail用户可以向Mailbox.org用户发送E2EE信息，或者你可以从支持OpenPGP的互联网服务中收到OpenPGP加密的通知。
|
||||
These providers natively support OpenPGP encryption/decryption and the [Web Key Directory (WKD) standard](basics/email-security.md#what-is-the-web-key-directory-standard), allowing for provider-agnostic E2EE emails. 例如，Proton Mail用户可以向Mailbox.org用户发送E2EE信息，或者你可以从支持OpenPGP的互联网服务中收到OpenPGP加密的通知。
|
||||
|
||||
<div class="grid cards" markdown>
|
||||
|
||||
@@ -107,7 +107,7 @@ Certain information stored in [Proton Contacts](https://proton.me/support/proton
|
||||
|
||||
#### :material-check:{ .pg-green } Email Encryption
|
||||
|
||||
Proton Mail has [integrated OpenPGP encryption](https://proton.me/support/how-to-use-pgp) in their webmail. 给其他Proton Mail账户的邮件是自动加密的，用OpenPGP密钥给非Proton Mail地址加密可以在账户设置中轻松启用。 Proton also supports automatic external key discovery with [Web Key Directory (WKD)](https://wiki.gnupg.org/WKD). This means that emails sent to other providers which use WKD will be automatically encrypted with OpenPGP as well, without the need to manually exchange public PGP keys with your contacts. They also allow you to [encrypt messages to non-Proton Mail addresses without OpenPGP](https://proton.me/support/password-protected-emails), without the need for them to sign up for a Proton Mail account.
|
||||
Proton Mail has [integrated OpenPGP encryption](https://proton.me/support/how-to-use-pgp) in their webmail. 给其他Proton Mail账户的邮件是自动加密的，用OpenPGP密钥给非Proton Mail地址加密可以在账户设置中轻松启用。 Proton also supports automatic external key discovery with WKD. This means that emails sent to other providers which use WKD will be automatically encrypted with OpenPGP as well, without the need to manually exchange public PGP keys with your contacts. They also allow you to [encrypt messages to non-Proton Mail addresses without OpenPGP](https://proton.me/support/password-protected-emails), without the need for them to sign up for a Proton Mail account.
|
||||
|
||||
Proton Mail also publishes the public keys of Proton accounts via HTTP from their WKD. 这使得不使用Proton Mail的人可以轻松找到Proton Mail账户的OpenPGP密钥，实现跨供应商的E2EE。 This only applies to email addresses ending in one of Proton's own domains, like @proton.me. If you use a custom domain, you must [configure WKD](./basics/email-security.md#what-is-the-web-key-directory-standard) separately.
|
||||
|
||||
@@ -164,7 +164,7 @@ However, [Open-Exchange](https://en.wikipedia.org/wiki/Open-Xchange), the softwa
|
||||
|
||||
Mailbox.org has [integrated encryption](https://kb.mailbox.org/en/private/e-mail-article/send-encrypted-e-mails-with-guard) in their webmail, which simplifies sending messages to people with public OpenPGP keys. They also allow [remote recipients to decrypt an email](https://kb.mailbox.org/en/private/e-mail-article/my-recipient-does-not-use-pgp) on Mailbox.org's servers. This feature is useful when the remote recipient does not have OpenPGP and cannot decrypt a copy of the email in their own mailbox.
|
||||
|
||||
Mailbox.org also supports the discovery of public keys via HTTP from their [Web Key Directory (WKD)](https://wiki.gnupg.org/WKD). This allows people outside of Mailbox.org to find the OpenPGP keys of Mailbox.org accounts easily, for cross-provider E2EE. This only applies to email addresses ending in one of Mailbox.org's own domains, like @mailbox.org. If you use a custom domain, you must [configure WKD](./basics/email-security.md#what-is-the-web-key-directory-standard) separately.
|
||||
Mailbox.org also supports the discovery of public keys via HTTP from their WKD. This allows people outside of Mailbox.org to find the OpenPGP keys of Mailbox.org accounts easily, for cross-provider E2EE. This only applies to email addresses ending in one of Mailbox.org's own domains, like @mailbox.org. If you use a custom domain, you must [configure WKD](./basics/email-security.md#what-is-the-web-key-directory-standard) separately.
|
||||
|
||||
#### :material-information-outline:{ .pg-blue } Account Termination
|
||||
|
||||
@@ -323,7 +323,7 @@ We regard these features as important in order to provide a safe and optimal ser
|
||||
|
||||
- Encrypts all account data (Contacts, Calendars, etc.) at rest with zero-access encryption.
|
||||
- Integrated webmail E2EE/PGP encryption provided as a convenience.
|
||||
- Support for [WKD](https://wiki.gnupg.org/WKD) to allow improved discovery of public OpenPGP keys via HTTP. GnuPG users can get a key by typing: `gpg --locate-key example_user@example.com`
|
||||
- Support for WKD to allow improved discovery of public OpenPGP keys via HTTP. GnuPG users can get a key by typing: `gpg --locate-key example_user@example.com`
|
||||
- Support for a temporary mailbox for external users. This is useful when you want to send an encrypted email, without sending an actual copy to your recipient. These emails usually have a limited lifespan and then are automatically deleted. They also don't require the recipient to configure any cryptography like OpenPGP.
|
||||
- Availability of the email provider's services via an [onion service](https://en.wikipedia.org/wiki/.onion).
|
||||
- [Sub-addressing](https://en.wikipedia.org/wiki/Email_address#Sub-addressing) support.
|
||||
|
||||
@@ -356,7 +356,7 @@ We [recommend](dns.md#recommended-providers) a number of encrypted DNS servers b
|
||||
<div class="grid cards" markdown>
|
||||
|
||||
- { .twemoji loading=lazy } [Kobold.cpp](ai-chat.md#koboldcpp)
|
||||
- { .twemoji loading=lazy } [Llamafile](ai-chat.md#llamafile)
|
||||
- { .twemoji loading=lazy } [Llamafile](ai-chat.md#llamafile)
|
||||
- { .twemoji loading=lazy } [Ollama (CLI)](ai-chat.md#ollama-cli)
|
||||
|
||||
</div>
|
||||
|
||||
Reference in New Issue
Block a user