Various docs fixes (#1429)

Follow up of #1343

In this PR I added:
- all models to the API reference (we had only openai and transformers
before)
- fixes to the docstrings that were not following the numpy style
- fixes to docstrings wrongly rendered by mkdocstring

Thanks :)

docs/api/models.md

@@ -1,3 +1 @@
-::: outlines.models.transformers
-
-::: outlines.models.openai
+::: outlines.models

docs/community/contribute.md

@@ -48,13 +48,6 @@ conda env create -f environment.yml
 
 Then install the dependencies in editable mode, and install the `pre-commit` hooks:
 
-```shell
-python -m venv .venv
-source .venv/bin/activate
-```
-
-Then install the dependencies in editable mode, and install the pre-commit hooks:
-
 ```shell
 pip install -e ".[test]"
 pre-commit install

outlines/models/exllamav2.py

@@ -118,9 +118,11 @@ def reformat_output(
         self, output: Union[str, List[str]], sampling_parameters: SamplingParameters
     ):
         """
-        The purpose of this function is to reformat the output from exllamav2's output format to outline's output format
-        For exllamav2, it mainly accepts only a list or a string(they also do cfg sampling with tuples but we will ignore this for now)
-        The exllamav2's logic is
+        The purpose of this function is to reformat the output from exllamav2's output format to outline's output format.
+
+        For exllamav2, it mainly accepts only a list or a string(they also do cfg sampling with tuples but we will ignore this for now).
+        The exllamav2's logic is:
+
         1. If the prompt is a string, return a string. This is the same as outlines
         2. If a prompt is a list, return a list. This is not the same as outlines output in that if the list is only one element, the string is expected to be outputted.
         3. There is no such thing as num_samples, so the prompts had to be duplicated by num_samples times. Then, we had the function output a list of lists

outlines/models/mlxlm.py

@@ -49,8 +49,8 @@ def stream(
     ) -> Iterator[str]:
         """Generate text using `mlx_lm`.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         generation_parameters
@@ -63,6 +63,7 @@ def stream(
             An instance of `SamplingParameters`, a dataclass that contains
             the name of the sampler to use and related parameters as available
             in Outlines.
+
         Returns
         -------
         The generated text.
@@ -135,14 +136,18 @@ def generate_step(
 
         A generator producing token ids based on the given prompt from the model.
 
-            Args:
-                prompt (mx.array): The input prompt.
-                temp (float): The temperature for sampling, if 0 the argmax is used.
-                  Default: ``0``.
-                top_p (float, optional): Nulceus sampling, higher means model considers
-                  more less likely words.
-                sampler (str): The sampler string defined by SequenceGeneratorAdapter
-                logits_processor (OutlinesLogitsProcessor): Augment logits before sampling.
+        Parameters
+        ----------
+        prompt
+            The input prompt.
+        temp
+            The temperature for sampling, if 0 the argmax is used.
+        top_p
+            Nulceus sampling, higher means model considers more less likely words.
+        sampler
+            The sampler string defined by SequenceGeneratorAdapter
+        logits_processor
+            Augment logits before sampling.
         """
         import mlx.core as mx
         import mlx_lm

outlines/models/vllm.py

@@ -52,8 +52,8 @@ def generate(
     ):
         """Generate text using vLLM.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         generation_parameters
@@ -171,7 +171,7 @@ def load_lora(self, adapter_path: Optional[str]):
 def vllm(model_name: str, **vllm_model_params):
     """Load a vLLM model.
 
-    Arguments
+    Parameters
     ---------
     model_name
         The name of the model to load from the HuggingFace hub.

outlines/prompts.py

@@ -152,42 +152,48 @@ def prompt(
     manipulation by providing some degree of encapsulation. It uses the `render`
     function internally to render templates.
 
+    ```pycon
     >>> import outlines
     >>>
     >>> @outlines.prompt
     >>> def build_prompt(question):
     ...    "I have a ${question}"
     ...
     >>> prompt = build_prompt("How are you?")
+    ```
 
     This API can also be helpful in an "agent" context where parts of the prompt
     are set when the agent is initialized and never modified later. In this situation
     we can partially apply the prompt function at initialization.
 
+    ```pycon
     >>> import outlines
     >>> import functools as ft
     ...
     >>> @outlines.prompt
     ... def solve_task(name: str, objective: str, task: str):
-    ...     '''Your name is {{name}}.
-    ..      Your overall objective is to {{objective}}.
+    ...     \"""Your name is {{name}}.
+    ...     Your overall objective is to {{objective}}.
     ...     Please solve the following task: {{task}}
-    ...     '''
+    ...     \"""
     ...
     >>> hal = ft.partial(solve_task, "HAL", "Travel to Jupiter")
+    ```
 
     Additional Jinja2 filters can be provided as keyword arguments to the decorator.
 
+    ```pycon
     >>> def reverse(s: str) -> str:
     ...     return s[::-1]
     ...
     >>> @outlines.prompt(filters={ 'reverse': reverse })
     ... def reverse_prompt(text):
-    ...     '''{{ text | reverse }}'''
+    ...     \"""{{ text | reverse }}\"""
     ...
     >>> prompt = reverse_prompt("Hello")
     >>> print(prompt)
     ... "olleH"
+    ```
 
     Returns
     -------