Contribute to the documentation (#630)

docs/source/zh/reference/agents.md

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+
 <!--Copyright 2024 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -13,41 +14,37 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
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-# Agents
+# Agents（智能体）
 
 <Tip warning={true}>
 
-Smolagents is an experimental API which is subject to change at any time. Results returned by the agents
-can vary as the APIs or underlying models are prone to change.
+Smolagents 是一个实验性的 API，可能会随时发生变化。由于 API 或底层模型可能发生变化，代理返回的结果也可能有所不同。
 
 </Tip>
 
-To learn more about agents and tools make sure to read the [introductory guide](../index). This page
-contains the API docs for the underlying classes.
-
-## Agents
+要了解有关智能体和工具的更多信息，请务必阅读[入门指南](../index)。本页面包含基础类的 API 文档。
 
-Our agents inherit from [`MultiStepAgent`], which means they can act in multiple steps, each step consisting of one thought, then one tool call and execution. Read more in [this conceptual guide](../conceptual_guides/react).
+## 智能体（Agents）
 
-We provide two types of agents, based on the main [`Agent`] class.
-  - [`CodeAgent`] is the default agent, it writes its tool calls in Python code.
-  - [`ToolCallingAgent`] writes its tool calls in JSON.
+我们的智能体继承自 [`MultiStepAgent`]，这意味着它们可以执行多步操作，每一步包含一个思考（thought），然后是一个工具调用和执行。请阅读[概念指南](../conceptual_guides/react)以了解更多信息。
 
-Both require arguments `model` and list of tools `tools` at initialization.
+我们提供两种类型的代理，它们基于主要的 [`Agent`] 类：
+  - [`CodeAgent`] 是默认代理，它以 Python 代码编写工具调用。
+  - [`ToolCallingAgent`] 以 JSON 编写工具调用。
 
+两者在初始化时都需要提供参数 `model` 和工具列表 `tools`。
 
-### Classes of agents
+### 智能体类
 
 [[autodoc]] MultiStepAgent
 
 [[autodoc]] CodeAgent
 
 [[autodoc]] ToolCallingAgent
 
-
 ### ManagedAgent
 
-_This class is deprecated since 1.8.0: now you just need to pass name and description attributes to an agent to directly use it as previously done with a ManagedAgent._
+_此类自 1.8.0 起已被弃用：现在您只需向普通代理传递 `name` 和 `description` 属性即可使其可被管理代理调用。_
 
 ### stream_to_gradio
 
@@ -56,99 +53,11 @@ _This class is deprecated since 1.8.0: now you just need to pass name and descri
 ### GradioUI
 
 > [!TIP]
-> You must have `gradio` installed to use the UI. Please run `pip install smolagents[gradio]` if it's not the case.
+> 您必须安装 `gradio` 才能使用 UI。如果尚未安装，请运行 `pip install smolagents[gradio]`。
 
 [[autodoc]] GradioUI
 
-## Models
-
-You're free to create and use your own models to power your agent.
-
-You could use any `model` callable for your agent, as long as:
-1. It follows the [messages format](./chat_templating) (`List[Dict[str, str]]`) for its input `messages`, and it returns a `str`.
-2. It stops generating outputs *before* the sequences passed in the argument `stop_sequences`
-
-For defining your LLM, you can make a `custom_model` method which accepts a list of [messages](./chat_templating) and returns text. This callable also needs to accept a `stop_sequences` argument that indicates when to stop generating.
-
-```python
-from huggingface_hub import login, InferenceClient
-
-login("<YOUR_HUGGINGFACEHUB_API_TOKEN>")
-
-model_id = "meta-llama/Llama-3.3-70B-Instruct"
-
-client = InferenceClient(model=model_id)
-
-def custom_model(messages, stop_sequences=["Task"]) -> str:
-    response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000)
-    answer = response.choices[0].message.content
-    return answer
-```
-
-Additionally, `custom_model` can also take a `grammar` argument. In the case where you specify a `grammar` upon agent initialization, this argument will be passed to the calls to model, with the `grammar` that you defined upon initialization, to allow [constrained generation](https://huggingface.co/docs/text-generation-inference/conceptual/guidance) in order to force properly-formatted agent outputs.
-
-### TransformersModel
-
-For convenience, we have added a `TransformersModel` that implements the points above by building a local `transformers` pipeline for the model_id given at initialization.
-
-```python
-from smolagents import TransformersModel
-
-model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
-
-print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))
-```
-```text
->>> What a
-```
-
-> [!TIP]
-> You must have `transformers` and `torch` installed on your machine. Please run `pip install smolagents[transformers]` if it's not the case.
-
-[[autodoc]] TransformersModel
-
-### HfApiModel
-
-The `HfApiModel` wraps an [HF Inference API](https://huggingface.co/docs/api-inference/index) client for the execution of the LLM.
-
-```python
-from smolagents import HfApiModel
-
-messages = [
-  {"role": "user", "content": "Hello, how are you?"},
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
-]
-
-model = HfApiModel()
-print(model(messages))
-```
-```text
->>> Of course! If you change your mind, feel free to reach out. Take care!
-```
-[[autodoc]] HfApiModel
-
-### LiteLLMModel
-
-The `LiteLLMModel` leverages [LiteLLM](https://www.litellm.ai/) to support 100+ LLMs from various providers.
-You can pass kwargs upon model initialization that will then be used whenever using the model, for instance below we pass `temperature`.
-
-```python
-from smolagents import LiteLLMModel
-
-messages = [
-  {"role": "user", "content": "Hello, how are you?"},
-  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-  {"role": "user", "content": "No need to help, take it easy."},
-]
-
-model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2, max_tokens=10)
-print(model(messages))
-```
-
-[[autodoc]] LiteLLMModel
-
-## Prompts
+## 提示（Prompts）
 
 [[autodoc]] smolagents.agents.PromptTemplates
 

docs/source/zh/reference/models.md

@@ -0,0 +1,166 @@
+
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+# 模型
+
+<Tip warning={true}>
+
+Smolagents 是一个实验性 API，其可能会随时发生更改。由于 API 或底层模型可能会变化，智能体返回的结果可能会有所不同。
+
+</Tip>
+
+要了解有关智能体和工具的更多信息，请务必阅读[入门指南](../index)。此页面包含底层类的 API 文档。
+
+## 模型
+
+您可以自由创建和使用自己的模型为智能体提供支持。
+
+您可以使用任何 `model` 可调用对象作为智能体的模型，只要满足以下条件：
+1. 它遵循[消息格式](./chat_templating)（`List[Dict[str, str]]`），将其作为输入 `messages`，并返回一个 `str`。
+2. 它在生成的序列到达 `stop_sequences` 参数中指定的内容之前停止生成输出。
+
+要定义您的 LLM，可以创建一个 `custom_model` 方法，该方法接受一个 [messages](./chat_templating) 列表，并返回一个包含 `.content` 属性的对象，其中包含生成的文本。此可调用对象还需要接受一个 `stop_sequences` 参数，用于指示何时停止生成。
+
+```python
+from huggingface_hub import login, InferenceClient
+
+login("<YOUR_HUGGINGFACEHUB_API_TOKEN>")
+
+model_id = "meta-llama/Llama-3.3-70B-Instruct"
+
+client = InferenceClient(model=model_id)
+
+def custom_model(messages, stop_sequences=["Task"]):
+    response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000)
+    answer = response.choices[0].message
+    return answer
+```
+
+此外，`custom_model` 还可以接受一个 `grammar` 参数。如果在智能体初始化时指定了 `grammar`，则此参数将在调用模型时传递，以便进行[约束生成](https://huggingface.co/docs/text-generation-inference/conceptual/guidance)，从而强制生成格式正确的智能体输出。
+
+### TransformersModel
+
+为了方便起见，我们添加了一个 `TransformersModel`，该模型通过为初始化时指定的 `model_id` 构建一个本地 `transformers` pipeline 来实现上述功能。
+
+```python
+from smolagents import TransformersModel
+
+model = TransformersModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
+
+print(model([{"role": "user", "content": [{"type": "text", "text": "Ok!"}]}], stop_sequences=["great"]))
+```
+```text
+>>> What a
+```
+
+> [!TIP]
+> 您必须在机器上安装 `transformers` 和 `torch`。如果尚未安装，请运行 `pip install smolagents[transformers]`。
+
+[[autodoc]] TransformersModel
+
+### HfApiModel
+
+`HfApiModel` 封装了 huggingface_hub 的 [InferenceClient](https://huggingface.co/docs/huggingface_hub/main/en/guides/inference)，用于执行 LLM。它支持 HF 的 [Inference API](https://huggingface.co/docs/api-inference/index) 以及 Hub 上所有可用的[Inference Providers](https://huggingface.co/blog/inference-providers)。
+
+```python
+from smolagents import HfApiModel
+
+messages = [
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
+]
+
+model = HfApiModel()
+print(model(messages))
+```
+```text
+>>> Of course! If you change your mind, feel free to reach out. Take care!
+```
+[[autodoc]] HfApiModel
+
+### LiteLLMModel
+
+`LiteLLMModel` 利用 [LiteLLM](https://www.litellm.ai/) 支持来自不同提供商的 100+ 个 LLM。您可以在模型初始化时传递 `kwargs`，这些参数将在每次使用模型时被使用，例如下面的示例中传递了 `temperature`。
+
+```python
+from smolagents import LiteLLMModel
+
+messages = [
+  {"role": "user", "content": [{"type": "text", "text": "Hello, how are you?"}]}
+]
+
+model = LiteLLMModel("anthropic/claude-3-5-sonnet-latest", temperature=0.2, max_tokens=10)
+print(model(messages))
+```
+
+[[autodoc]] LiteLLMModel
+
+### OpenAIServerModel
+
+此类允许您调用任何 OpenAIServer 兼容模型。
+以下是设置方法（您可以自定义 `api_base` URL 指向其他服务器）：
+```py
+import os
+from smolagents import OpenAIServerModel
+
+model = OpenAIServerModel(
+    model_id="gpt-4o",
+    api_base="https://api.openai.com/v1",
+    api_key=os.environ["OPENAI_API_KEY"],
+)
+```
+
+[[autodoc]] OpenAIServerModel
+
+### AzureOpenAIServerModel
+
+`AzureOpenAIServerModel` 允许您连接到任何 Azure OpenAI 部署。
+
+下面是设置示例，请注意，如果已经设置了相应的环境变量，您可以省略 `azure_endpoint`、`api_key` 和 `api_version` 参数——环境变量包括 `AZURE_OPENAI_ENDPOINT`、`AZURE_OPENAI_API_KEY` 和 `OPENAI_API_VERSION`。
+
+请注意，`OPENAI_API_VERSION` 没有 `AZURE_` 前缀，这是由于底层 [openai](https://github.com/openai/openai-python) 包的设计所致。
+
+```py
+import os
+
+from smolagents import AzureOpenAIServerModel
+
+model = AzureOpenAIServerModel(
+    model_id = os.environ.get("AZURE_OPENAI_MODEL"),
+    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+    api_key=os.environ.get("AZURE_OPENAI_API_KEY"),
+    api_version=os.environ.get("OPENAI_API_VERSION")    
+)
+```
+
+[[autodoc]] AzureOpenAIServerModel
+
+### MLXModel
+
+```python
+from smolagents import MLXModel
+
+model = MLXModel(model_id="HuggingFaceTB/SmolLM-135M-Instruct")
+
+print(model([{"role": "user", "content": "Ok!"}], stop_sequences=["great"]))
+```
+```text
+>>> What a
+```
+
+> [!TIP]
+> 您必须在机器上安装 `mlx-lm`。如果尚未安装，请运行 `pip install smolagents[mlx-lm]`。
+
+[[autodoc]] MLXModel