Run pre-commit to the examples

docs/cookbook/chain_of_thought.md

@@ -16,7 +16,7 @@ We download the model weights by passing the name of the repository on the Huggi
 import llama_cpp
 from outlines import generate, models
 
-model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", 
+model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
             "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
             tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
             "NousResearch/Hermes-2-Pro-Llama-3-8B"
@@ -30,18 +30,18 @@ model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
 ??? note "(Optional) Store the model weights in a custom folder"
 
     By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/):
-    
+
     ```bash
     wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
     ```
-    
+
     We initialize the model:
-    
+
     ```python
     import llama_cpp
     from llama_cpp import Llama
     from outlines import generate, models
-    
+
     llm = Llama(
         "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
         tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(

docs/cookbook/knowledge_graph_extraction.md

@@ -13,7 +13,7 @@ We download the model weights by passing the name of the repository on the Huggi
 import llama_cpp
 from outlines import generate, models
 
-model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", 
+model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
             "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
             tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
             "NousResearch/Hermes-2-Pro-Llama-3-8B"
@@ -27,18 +27,18 @@ model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
 ??? note "(Optional) Store the model weights in a custom folder"
 
     By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/):
-    
+
     ```bash
     wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
     ```
-    
+
     We initialize the model:
-    
+
     ```python
     import llama_cpp
     from llama_cpp import Llama
     from outlines import generate, models
-    
+
     llm = Llama(
         "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
         tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(

docs/cookbook/qa-with-citations.md

@@ -13,7 +13,7 @@ We download the model weights by passing the name of the repository on the Huggi
 import llama_cpp
 from outlines import generate, models
 
-model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", 
+model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
             "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
             tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
             "NousResearch/Hermes-2-Pro-Llama-3-8B"
@@ -27,18 +27,18 @@ model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
 ??? note "(Optional) Store the model weights in a custom folder"
 
     By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/):
-    
+
     ```bash
     wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
     ```
-    
+
     We initialize the model:
-    
+
     ```python
     import llama_cpp
     from llama_cpp import Llama
     from outlines import generate, models
-    
+
     llm = Llama(
         "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
         tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(

docs/cookbook/react_agent.md

@@ -17,7 +17,7 @@ We download the model weights by passing the name of the repository on the Huggi
 import llama_cpp
 from outlines import generate, models
 
-model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF", 
+model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
             "Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
             tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
             "NousResearch/Hermes-2-Pro-Llama-3-8B"
@@ -31,18 +31,18 @@ model = models.llamacpp("NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF",
 ??? note "(Optional) Store the model weights in a custom folder"
 
     By default the model weights are downloaded to the hub cache but if we want so store the weights in a custom folder, we pull a quantized GGUF model [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF) by [NousResearch](https://nousresearch.com/) from [HuggingFace](https://huggingface.co/):
-    
+
     ```bash
     wget https://hf.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF/resolve/main/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf
     ```
-    
+
     We initialize the model:
-    
+
     ```python
     import llama_cpp
     from llama_cpp import Llama
     from outlines import generate, models
-    
+
     llm = Llama(
         "/path/to/model/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf",
         tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(