Update CLI example (#544)

README.md

@@ -102,19 +102,20 @@ from distilabel.pipeline import Pipeline
 from distilabel.steps import LoadHubDataset, TextGenerationToArgilla
 from distilabel.steps.tasks import TextGeneration
 
-with Pipeline("pipe-name", description="My first pipe") as pipeline:
+with Pipeline(
+    name="simple-text-generation-pipeline",
+    description="A simple text generation pipeline",
+) as pipeline:
     load_dataset = LoadHubDataset(
         name="load_dataset",
         output_mappings={"prompt": "instruction"},
     )
 
     generate_with_openai = TextGeneration(
-        name="generate_with_openai", llm=OpenAILLM(model="gpt-4-0125-preview")
+        name="generate_with_gpt3.5", llm=OpenAILLM(model="gpt-3.5-turbo")
     )
 
-    to_argilla = TextGenerationToArgilla(
-        name="to_argilla", dataset_name="text-generation-with-gpt4"
-    )
+    to_argilla = TextGenerationToArgilla(name="to_argilla")
 
     load_dataset.connect(generate_with_openai)
     generate_with_openai.connect(to_argilla)
@@ -127,7 +128,7 @@ if __name__ == "__main__":
                 "repo_id": "distilabel-internal-testing/instruction-dataset-mini",
                 "split": "test",
             },
-            "generate_with_openai": {
+            "generate_with_gpt35": {
                 "llm": {
                     "generation_kwargs": {
                         "temperature": 0.7,
@@ -136,8 +137,8 @@ if __name__ == "__main__":
                 }
             },
             "to_argilla": {
-                "api_url": "https://cloud.argilla.io",
-                "api_key": "i.love.argilla",
+                "dataset_name": "text-generations-with-gpt35",
+                "dataset_workspace": "admin",
             },
         },
     )

docs/index.md

@@ -40,19 +40,20 @@ from distilabel.pipeline import Pipeline
 from distilabel.steps import LoadHubDataset, TextGenerationToArgilla
 from distilabel.steps.tasks import TextGeneration
 
-with Pipeline("pipe-name", description="My first pipe") as pipeline:
+with Pipeline(
+    name="simple-text-generation-pipeline",
+    description="A simple text generation pipeline",
+) as pipeline:
     load_dataset = LoadHubDataset(
         name="load_dataset",
         output_mappings={"prompt": "instruction"},
     )
 
     generate_with_openai = TextGeneration(
-        name="generate_with_openai", llm=OpenAILLM(model="gpt-4-0125-preview")
+        name="generate_with_gpt3.5", llm=OpenAILLM(model="gpt-3.5-turbo")
     )
 
-    to_argilla = TextGenerationToArgilla(
-        name="to_argilla", dataset_name="text-generation-with-gpt4"
-    )
+    to_argilla = TextGenerationToArgilla(name="to_argilla")
 
     load_dataset.connect(generate_with_openai)
     generate_with_openai.connect(to_argilla)
@@ -65,7 +66,7 @@ if __name__ == "__main__":
                 "repo_id": "distilabel-internal-testing/instruction-dataset-mini",
                 "split": "test",
             },
-            "generate_with_openai": {
+            "generate_with_gpt35": {
                 "llm": {
                     "generation_kwargs": {
                         "temperature": 0.7,
@@ -74,8 +75,8 @@ if __name__ == "__main__":
                 }
             },
             "to_argilla": {
-                "api_url": "https://cloud.argilla.io",
-                "api_key": "i.love.argilla",
+                "dataset_name": "text-generations-with-gpt35",
+                "dataset_workspace": "admin",
             },
         },
     )

docs/sections/learn/cli.md

@@ -46,14 +46,12 @@ $ distilabel pipeline info --help
 As we can see from the help message, we need to pass either a `Path` or a `URL`. This second option comes handy for datasets stored in Hugging Face Hub, for example:
 
 ```bash
-distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/ultrafeedback-mini/raw/main/pipeline.yaml"
+distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
 ```
 
-If we take a look (this `Pipeline` is a bit long, so the following captures are shortened for brevity):
+If we take a look:
 
-![CLI 1](../../assets/images/sections/cli/cli_pipe_1.png)
-
-![CLI 2](../../assets/images/sections/cli/cli_pipe_2.png)
+![CLI 1](../../assets/images/sections/cli/cli_pipe.png)
 
 The pipeline information includes the steps used in the `Pipeline` along with the `Runtime Parameter` that was used, as well as a description of each of them, and also the connections between these steps. These can be helpful to explore the Pipeline locally.
 
@@ -93,15 +91,13 @@ $ distilabel pipeline run --help
 To specify the runtime parameters of the steps we will need to use the `--param` option and the value of the parameter in the following format:
 
 ```bash
-distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/ultrafeedback-mini/raw/main/pipeline.yaml" \
-  --param load_dataset.repo_id=HuggingFaceH4/instruction-dataset \
-  --param load_dataset.split=test \
-  --param text_generation_with_notus.generation_kwargs.max_new_tokens=512 \
-  --param text_generation_with_notus.generation_kwargs.temperature=0.7 \
-  --param text_generation_with_zephyr.generation_kwargs.max_new_tokens=512 \
-  --param text_generation_with_zephyr.generation_kwargs.temperature=0.7 \
-  --param ultrafeedback_overall_rating.generation_kwargs.max_new_tokens=1024 \
-  --param ultrafeedback_overall_rating.generation_kwargs.temperature=0.7
+distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml" \
+	--param load_dataset.repo_id=distilabel-internal-testing/instruction-dataset-mini \
+	--param load_dataset.split=test \
+	--param generate_with_gpt35.llm.generation_kwargs.max_new_tokens=512 \
+	--param generate_with_gpt35.llm.generation_kwargs.temperature=0.7 \
+	--param to_argilla.dataset_name=text_generation_with_gpt35 \
+	--param to_argilla.dataset_workspace=admin
 ```
 
 Again, this helps with the reproducibility of the results, and simplifies sharing not only the final dataset but also the process to generate it.