Various docs fixes

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/llamacpp.py

@@ -248,8 +248,8 @@ def generate(
     ) -> str:
         """Generate text using `llama-cpp-python`.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         generation_parameters
@@ -302,8 +302,8 @@ def stream(
     ) -> Iterator[str]:
         """Stream text using `llama-cpp-python`.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         generation_parameters
@@ -372,8 +372,8 @@ def llamacpp(
     a path to the downloaded model. One can still load a local model
     by initializing `llama_cpp.Llama` directly.
 
-    Arguments
-    ---------
+    Parameters
+    ----------
     repo_id
         The name of the model repository.
     filename:

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/openai.py

@@ -20,11 +20,11 @@ class OpenAIConfig:
     properties that are specific to the OpenAI API. Not all these properties are
     supported by Outlines.
 
-    Properties
+    Parameters
     ----------
     model
         The name of the model. Available models can be found on OpenAI's website.
-    frequence_penalty
+    frequency_penalty
         Number between 2.0 and -2.0. Positive values penalize new tokens based on
         their existing frequency in the text,
     logit_bias
@@ -49,7 +49,6 @@ class OpenAIConfig:
         Number between 0 and 1. Parameter for nucleus sampling.
     user
         A unique identifier for the end-user.
-
     """
 
     model: str = ""

outlines/models/transformers.py

@@ -203,8 +203,8 @@ def generate(
     ) -> Union[str, List[str], List[List[str]]]:
         """Generate text using `transformers`.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         generation_parameters
@@ -304,7 +304,7 @@ def _get_generation_kwargs(
         sampling_parameters: SamplingParameters,
     ) -> dict:
         """
-        Conert outlines generation parameters into model.generate kwargs
+        Convert outlines generation parameters into model.generate kwargs
         """
         from transformers import GenerationConfig, LogitsProcessorList, set_seed
 

outlines/models/transformers_vision.py

@@ -22,8 +22,8 @@ def generate(  # type: ignore
     ) -> Union[str, List[str], List[List[str]]]:
         """Generate text using `transformers`.
 
-        Arguments
-        ---------
+        Parameters
+        ----------
         prompts
             A prompt or list of prompts.
         media

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

@@ -125,42 +125,48 @@ 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
     -------