Fix typos

Author: rlouf

docs/features/core/output_types.md

@@ -13,7 +13,7 @@ Outlines models accept a __prompt__ and an __output type__ when they are invoked
 Output types can be from the general Python ecosystem, including:
 - Most native Python types, such as `int` or `str`
 - Types from the `typing` module, such as `Literal`, `List`, `Dict`, `Enum`, etc
-- Types from popular theird party libraries such as Pydantic or GenSON.
+- Types from popular third party libraries such as Pydantic or GenSON.
 
 Outlines also provides special classes for certain output structures (more details below):
 - JSON schemas with `JsonSchema`
@@ -164,7 +164,7 @@ output_type = JsonSchema(schema_dict)
 
 ### Regex Patterns
 
-Outlines provides support for text generation constrained by regular expressions. Since regular expressions are expressed as simple raw string literals, regex strings must wrapped in an `outlines.types.Regex` object to clarify the expected return type.
+Outlines provides support for text generation constrained by regular expressions. Since regular expressions are expressed as simple raw string literals, regex strings must wrapped in an `outlines.types.Regex` object.
 
 ```python
 from outlines.types import Regex

docs/features/index.md

@@ -8,7 +8,7 @@ This section presents in details the different features of Outlines.
 - [Output Types](./core/output_types.md)
 - [Generators](./core/generator.md)
 
-## Utility
+## Utilities
 
 - [Applications](./utility/application.md)
 - [Templates](./utility/templates.md)

docs/features/models/index.md

@@ -45,6 +45,8 @@ print(result) # '200'
 
 ## Features Matrix
 
+In alphabetical order:
+
 | | [Anthropic](../../models/anthropic) | [Dottxt](../../models/dottxt) | [Gemini](../../models/gemini) | [LlamaCpp](../../models/llamacpp) | [MLXLM](../../models/mlxlm) | [Ollama](../../models/ollama) | [OpenAI](../../models/openai) | [SGLang](../../models/sglang) | [TGI](../../models/tgi) | [Transformers](../../models/transformers) | [Transformers MultiModal](../../models/transformers_multimodal) | [VLLM](../../models/vllm) | [VLLMOffline](../../models/vllm_offline) |
 |---|---|---|---|---|---|---|---|---|---|---|---|---|---|
 | **Output Types** | | | | | | | | | | | | | |
@@ -63,7 +65,7 @@ print(result) # '200'
 
 Models can be divided into two categories: local models and server-based models.
 
-In the case of local models, the text generation happens within the inference library object used to instantite the model. This gives Outlines direct access to the generation process (through a logits processor) and means all structured generation output types are available.
+In the case of local models, the text generation happens within the inference library object used to instantiate the model. This gives Outlines direct access to the generation process (through a logits processor) and means all structured generation output types are available.
 
 The local models available are the following:
 

docs/features/utility/regex_dsl.md

@@ -329,7 +329,7 @@ print(pattern)
 
 *Expected Output:*
 
-```
+```ascii
 └── Sequence
     ├── String('a')
     ├── KleenePlus(+)

docs/guide/getting_started.md

@@ -28,45 +28,64 @@ The full list of available models along with detailed explanation on how to use
 
 For a quick start, you can find below an example of how to initialize all supported models in Outlines:
 
-=== "Anthropic"
+=== "vLLM"
 
     ```python
     import outlines
-    from anthropic import Anthropic
+    from openai import OpenAI
 
-    # Create an Anthropic client
-    anthropic_client = Anthropic()
+    # You must have a separate vLLM server running
+    # Create an OpenAI client with the base URL of the VLLM server
+    openai_client = OpenAI(base_url="http://localhost:11434/v1")
 
     # Create an Outlines model
-    model = outlines.from_anthropic(anthropic_client, "claude-3-haiku-20240307")
+    model = outlines.from_vllm(openai_client, "microsoft/Phi-3-mini-4k-instruct")
     ```
 
-=== "Dottxt"
+=== "Ollama"
 
     ```python
     import outlines
-    from dottxt.client import Dottxt
+    from ollama import Client
 
-    # Create an Dottxt client
-    dottxt_client = Dottxt()
+    # Create an Ollama client
+    ollama_client = Client()
+
+    # Create an Outlines model, the model must be available on your system
+    model = outlines.from_ollama(ollama_client, "tinyllama")
+    ```
+
+=== "OpenAI"
+
+    ```python
+    import outlines
+    from openai import OpenAI
+
+    # Create an OpenAI client instance
+    openai_client = OpenAI()
 
     # Create an Outlines model
-    model = outlines.from_dottxt(dottxt_client)
+    model = outlines.from_openai(openai_client, "gpt-4o")
     ```
 
-=== "Gemini"
+=== "Transformers"
 
     ```python
     import outlines
-    from google.generativeai import GenerativeModel
+    from transformers
 
-    # Create a Gemini client
-    gemini_client = GenerativeModel()
+    # Define the model you want to use
+    model_name = "HuggingFaceTB/SmolLM2-135M-Instruct"
+
+    # Create a HuggingFace model and tokenizer
+    hf_model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
+    hf_tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 
     # Create an Outlines model
-    model = outlines.from_gemini(gemini_client, "gemini-1-5-flash")
+    model = outlines.from_transformers(hf_model, hf_tokenizer)
     ```
 
+
 === "llama.cpp"
 
     ```python
@@ -84,43 +103,30 @@ For a quick start, you can find below an example of how to initialize all suppor
     model = outlines.from_llamacpp(llama_cpp_model)
     ```
 
-=== "mlx-lm"
-
-    ```python
-    import outlines
-    import mlx_lm
-
-    # Create an MLXLM model with the output of mlx_lm.load
-    # The model will be downloaded from the HuggingFace hub
-    model = outlines.from_mlxlm(
-        **mlx_lm.load("mlx-community/SmolLM-135M-Instruct-4bit")
-    )
-    ```
-
-=== "Ollama"
+=== "Gemini"
 
     ```python
     import outlines
-    from ollama import Client
+    from google.generativeai import GenerativeModel
 
-    # Create an Ollama client
-    ollama_client = Client()
+    # Create a Gemini client
+    gemini_client = GenerativeModel()
 
-    # Create an Outlines model, the model must be available on your system
-    model = outlines.from_ollama(ollama_client, "tinyllama")
+    # Create an Outlines model
+    model = outlines.from_gemini(gemini_client, "gemini-1-5-flash")
     ```
 
-=== "OpenAI"
+=== "mlx-lm"
 
     ```python
     import outlines
-    from openai import OpenAI
-
-    # Create an OpenAI client instance
-    openai_client = OpenAI()
+    import mlx_lm
 
-    # Create an Outlines model
-    model = outlines.from_openai(openai_client, "gpt-4o")
+    # Create an MLXLM model with the output of mlx_lm.load
+    # The model will be downloaded from the HuggingFace hub
+    model = outlines.from_mlxlm(
+        **mlx_lm.load("mlx-community/SmolLM-135M-Instruct-4bit")
+    )
     ```
 
 === "SgLang"
@@ -153,23 +159,6 @@ For a quick start, you can find below an example of how to initialize all suppor
     model = outlines.from_tgi(tgi_client)
     ```
 
-=== "Transformers"
-
-    ```python
-    import outlines
-    from transformers
-
-    # Define the model you want to use
-    model_name = "HuggingFaceTB/SmolLM2-135M-Instruct"
-
-    # Create a HuggingFace model and tokenizer
-    hf_model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
-    hf_tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-
-    # Create an Outlines model
-    model = outlines.from_transformers(hf_model, hf_tokenizer)
-    ```
-
 === "vLLM (offline)"
 
     ```python
@@ -183,19 +172,6 @@ For a quick start, you can find below an example of how to initialize all suppor
     model = outlines.from_vllm_offline(vllm_model)
     ```
 
-=== "vLLM (online)"
-
-    ```python
-    import outlines
-    from openai import OpenAI
-
-    # You must have a separate vLLM server running
-    # Create an OpenAI client with the base URL of the VLLM server
-    openai_client = OpenAI(base_url="http://localhost:11434/v1")
-
-    # Create an Outlines model
-    model = outlines.from_vllm(openai_client, "microsoft/Phi-3-mini-4k-instruct")
-    ```
 
 ## Generating Text
 
@@ -221,7 +197,7 @@ for chunk in model.streaming("Write a short story about a cat.")
 
 ## Structured Generation
 
-Outlines follows a simple pattern that mirrors Python's own type system for structured output. Simply specify the desired output type as you would when using type hinting with a function, and Outlines will ensure your data matches that structure exactly.
+Outlines follows a simple pattern that mirrors Python's own type system for structured outputs. Simply specify the desired output type as you would when using type hinting with a function, and Outlines will ensure your data matches that structure exactly.
 
 Supported output types can be organized in 5 categories:
 
@@ -235,7 +211,7 @@ Consult the section on [Output Types](../../features/core/output_types.md) in th
 
 In the meantime, you can find below examples of using each of the five output type categories:
 
-=== "Basic Yypes"
+=== "Basic Types"
 
     ```python
     model = <your_model_as_defined_above>
@@ -332,7 +308,7 @@ In the meantime, you can find below examples of using each of the five output ty
     print(result) # '2 + 3'
     ```
 
-It's important to note that not all output types are available for all models due to limitations in the underline inference libraries. The [Models](../features/models/index.md) section of the features documentation includes a features matrix to easily visualize output type availabilities.
+It's important to note that not all output types are available for all models due to limitations in the underlying inference engines. The [Models](../features/models/index.md) section of the features documentation includes a features matrix that summarize the availability of output types.
 
 ## Generators
 

docs/guide/installation.md

@@ -20,7 +20,7 @@ source .venv/bin/activate
 uv pip install outlines
 ```
 
-or with basic pip:
+or with pip:
 
 ```shell
 pip install outlines

docs/guide/migration.md

@@ -4,7 +4,7 @@ Outlines 1.0 introduces some breaking changes that affect the way you use the li
 
 This guide will help you migrate your code to the new version.
 
-All previous functionality will be supported until Outlines version 1.1.0, but a warning message will be displayed to remind you to migrate your code and provide instructions to help you do so. Please migrate your code to the v1 as soon as possible.
+All previous functionalities will be supported until Outlines version 1.1.0, but a warning message will be displayed to remind you to migrate your code and provide instructions to help you do so. Please migrate your code to the v1 as soon as possible.
 
 ## Removed or modified features
 - [Generate functions](#generate-functions)