Delete distiset_configs

distiset_configs/README.md

@@ -1,73 +0,0 @@
----
-size_categories: n<1K
-tags:
-- synthetic
-- distilabel
-- rlaif
----
-
-<p align="left">
-  <a href="https://github.com/argilla-io/distilabel">
-    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
-  </a>
-</p>
-
-# Dataset Card for love2dapi_queries
-
-This dataset has been created with [distilabel](https://distilabel.argilla.io/).
-
-## Dataset Summary
-
-This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
-
-```console
-distilabel pipeline run --config "https://huggingface.co/datasets/love2dapi_queries/raw/main/pipeline.yaml"
-```
-
-or explore the configuration:
-
-```console
-distilabel pipeline info --config "https://huggingface.co/datasets/love2dapi_queries/raw/main/pipeline.yaml"
-```
-
-## Dataset structure
-
-The examples have the following structure per configuration:
-
-
-<details><summary> Configuration: default </summary><hr>
-
-```json
-{
-    "anchor": "description: Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.\nhide: navigation\n\nWelcome to Argilla\n\nArgilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.",
-    "distilabel_metadata": {
-        "raw_output_multiply_queries": null
-    },
-    "filename": "argilla-python/docs/index.md",
-    "model_name_query": "meta-llama/Meta-Llama-3-70B-Instruct",
-    "model_name_query_multiplied": "meta-llama/Meta-Llama-3-70B-Instruct",
-    "negative": null,
-    "positive": null,
-    "queries": null,
-    "repo_name": "argilla-io/argilla-python"
-}
-```
-
-This subset can be loaded as:
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("love2dapi_queries", "default")
-```
-
-Or simply as it follows, since there's only one configuration and is named `default`: 
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("love2dapi_queries")
-```
-
-
-</details>

distiset_configs/pipeline.log

@@ -1,38 +0,0 @@
-[2024-07-19 16:51:10] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 16:51:10] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 16:53:15] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 16:53:15] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 17:03:37] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 17:03:37] WARNING  Since the `base_url=https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct` is available and either one of `model_id` or `endpoint_name` is also provided, the `base_url` will either be ignored or overwritten with the one generated from either of those args, for serverless or dedicated inference endpoints, respectively.
-[2024-07-19 17:03:52] WARNING  Task 'multiply_queries' failed to format output: 'NoneType' object has no attribute 'split'. Saving raw response.
-[2024-07-19 17:03:52] WARNING  Subprocess traceback:
-
-Traceback (most recent call last):
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\pipeline\local.py", line 512, in _non_generator_process_loop
-    result = next(self.step.process_applying_mappings(*batch.data))
-             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\steps\base.py", line 512, in process_applying_mappings
-    for output_rows in generator:
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\steps\combine.py", line 119, in process
-    yield combine_dicts(
-          ^^^^^^^^^^^^^^
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\pipeline\utils.py", line 39, in combine_dicts
-    raise ValueError(
-ValueError: The length of output_merge_keys must be the same as the length of merge_keys
-
-[2024-07-19 17:03:52] WARNING  Subprocess traceback:
-
-Traceback (most recent call last):
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\pipeline\local.py", line 512, in _non_generator_process_loop
-    result = next(self.step.process_applying_mappings(*batch.data))
-             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\steps\base.py", line 512, in process_applying_mappings
-    for output_rows in generator:
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\steps\expand.py", line 111, in process
-    yield [row for input in inputs for row in self._expand_columns(input)]
-                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "C:\Users\Andi\Python projects\RAGTesting\.venv\Lib\site-packages\distilabel\steps\expand.py", line 126, in _expand_columns
-    for item, expanded in zip_longest(*[data, expanded_rows], fillvalue=input):
-                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-TypeError: 'NoneType' object is not iterable
-

distiset_configs/pipeline.yaml

@@ -1,334 +0,0 @@
-distilabel:
-  version: 1.2.2
-pipeline:
-  connections:
-  - from: load_data
-    to:
-    - generate_sentence_pair
-  - from: generate_sentence_pair
-    to:
-    - multiply_queries
-  - from: multiply_queries
-    to:
-    - merge_columns
-  - from: merge_columns
-    to:
-    - expand_columns_0
-  - from: expand_columns_0
-    to: []
-  description: Generate queries to train a sentence embedding model.
-  name: embedding-queries
-  routing_batch_functions: []
-  steps:
-  - name: load_data
-    step:
-      batch_size: 10
-      config: null
-      input_mappings: {}
-      name: load_data
-      num_examples: null
-      output_mappings:
-        chunks: anchor
-      repo_id: Nocare3/love2dapi_chunks
-      runtime_parameters_info:
-      - description: The number of rows that will contain the batches generated by
-          the step.
-        name: batch_size
-        optional: true
-      - description: The Hugging Face Hub repository ID of the dataset to load.
-        name: repo_id
-        optional: false
-      - description: The split of the dataset to load. Defaults to 'train'.
-        name: split
-        optional: true
-      - description: The configuration of the dataset to load. This is optional and
-          only needed if the dataset has multiple configurations.
-        name: config
-        optional: true
-      - description: Whether to load the dataset in streaming mode or not. Defaults
-          to False.
-        name: streaming
-        optional: true
-      - description: The number of examples to load from the dataset. By default will
-          load all examples.
-        name: num_examples
-        optional: true
-      split: train
-      storage_options: null
-      streaming: false
-      type_info:
-        module: distilabel.steps.generators.huggingface
-        name: LoadDataFromHub
-  - name: generate_sentence_pair
-    step:
-      action: query
-      add_raw_output: true
-      context: The generated sentence has to be related with Love2d, a lua-code game
-        engine used mostly by indie developers.
-      group_generations: false
-      input_batch_size: 10
-      input_mappings: {}
-      llm:
-        base_url: null
-        endpoint_name: null
-        endpoint_namespace: null
-        generation_kwargs:
-          max_new_tokens: 512
-          temperature: 0.7
-        model_display_name: null
-        model_id: meta-llama/Meta-Llama-3-70B-Instruct
-        structured_output: null
-        tokenizer_id: meta-llama/Meta-Llama-3-70B-Instruct
-        type_info:
-          module: distilabel.llms.huggingface.inference_endpoints
-          name: InferenceEndpointsLLM
-        use_openai_client: false
-      name: generate_sentence_pair
-      num_generations: 1
-      output_mappings:
-        model_name: model_name_query
-      runtime_parameters_info:
-      - description: The number of rows that will contain the batches processed by
-          the step.
-        name: input_batch_size
-        optional: true
-      - name: llm
-        runtime_parameters_info:
-        - description: The kwargs to be propagated to either `generate` or `agenerate`
-            methods within each `LLM`.
-          keys:
-          - description: the maximum number of new tokens that the model will generate.  Defaults
-              to `128`.
-            name: max_new_tokens
-            optional: true
-          - description: the repetition penalty to use for the generation. Defaults  to
-              `0.0`. Only applies if `use_openai_client=True`.
-            name: frequency_penalty
-            optional: true
-          - description: the presence penalty to use for the generation. Defaults
-              to  `0.0`. Only applies if `use_openai_client=True`.
-            name: presence_penalty
-            optional: true
-          - description: the repetition penalty to use for the generation. Defaults  to
-              `None`. Only applies if `use_openai_client=False`.
-            name: repetition_penalty
-            optional: true
-          - description: the temperature to use for the generation. Defaults to `1.0`.
-            name: temperature
-            optional: true
-          - description: whether to use sampling for the generation. Defaults to `False`.  Only
-              applies if `use_openai_client=False`.
-            name: do_sample
-            optional: true
-          - description: the top-k value to use for the generation. Defaults to `0.8`,
-              since neither  `0.0` nor `1.0` are valid values in TGI.
-            name: top_k
-            optional: true
-          - description: the top-p value to use for the generation. Defaults to `1.0`.
-            name: top_p
-            optional: true
-          - description: the typical-p value to use for the generation. Defaults to
-              `0.5`.
-            name: typical_p
-            optional: true
-          - description: either a single string or a list of strings containing the
-              sequences  to stop the generation at. Defaults to `None`, but will be
-              set to the  `tokenizer.eos_token` if available.
-            name: stop_sequences
-            optional: true
-          - description: whether to return the full text of the completion or just
-              the  generated text. Defaults to `False`, meaning that only the generated
-              text will be  returned.
-            name: return_full_text
-            optional: true
-          - description: the seed to use for the generation. Defaults to `None`.
-            name: seed
-            optional: true
-          - description: whether to add the watermark to the generated text. Defaults
-              to `None`.
-            name: watermark
-            optional: true
-          name: generation_kwargs
-        - description: The name of the Inference Endpoint to use for the LLM.
-          name: endpoint_name
-          optional: true
-        - description: The namespace of the Inference Endpoint to use for the LLM.
-          name: endpoint_namespace
-          optional: true
-        - description: The base URL to use for the Inference Endpoints API requests.
-          name: base_url
-          optional: true
-        - description: The API key to authenticate the requests to the Inference Endpoints
-            API.
-          name: api_key
-          optional: true
-        - description: The structured output format to use across all the generations.
-          name: structured_output
-          optional: true
-      - description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
-          of the `distilabel_metadata` dictionary output column
-        name: add_raw_output
-        optional: true
-      - description: The number of generations to be produced per input.
-        name: num_generations
-        optional: true
-      triplet: true
-      type_info:
-        module: distilabel.steps.tasks.sentence_transformers
-        name: GenerateSentencePair
-  - name: multiply_queries
-    step:
-      add_raw_output: true
-      group_generations: false
-      input_batch_size: 10
-      input_mappings:
-        query: positive
-      llm:
-        base_url: null
-        endpoint_name: null
-        endpoint_namespace: null
-        generation_kwargs:
-          max_new_tokens: 512
-          temperature: 0.7
-        model_display_name: null
-        model_id: meta-llama/Meta-Llama-3-70B-Instruct
-        structured_output: null
-        tokenizer_id: meta-llama/Meta-Llama-3-70B-Instruct
-        type_info:
-          module: distilabel.llms.huggingface.inference_endpoints
-          name: InferenceEndpointsLLM
-        use_openai_client: false
-      name: multiply_queries
-      num_generations: 1
-      num_queries: 3
-      output_mappings:
-        model_name: model_name_query_multiplied
-      runtime_parameters_info:
-      - description: The number of rows that will contain the batches processed by
-          the step.
-        name: input_batch_size
-        optional: true
-      - name: llm
-        runtime_parameters_info:
-        - description: The kwargs to be propagated to either `generate` or `agenerate`
-            methods within each `LLM`.
-          keys:
-          - description: the maximum number of new tokens that the model will generate.  Defaults
-              to `128`.
-            name: max_new_tokens
-            optional: true
-          - description: the repetition penalty to use for the generation. Defaults  to
-              `0.0`. Only applies if `use_openai_client=True`.
-            name: frequency_penalty
-            optional: true
-          - description: the presence penalty to use for the generation. Defaults
-              to  `0.0`. Only applies if `use_openai_client=True`.
-            name: presence_penalty
-            optional: true
-          - description: the repetition penalty to use for the generation. Defaults  to
-              `None`. Only applies if `use_openai_client=False`.
-            name: repetition_penalty
-            optional: true
-          - description: the temperature to use for the generation. Defaults to `1.0`.
-            name: temperature
-            optional: true
-          - description: whether to use sampling for the generation. Defaults to `False`.  Only
-              applies if `use_openai_client=False`.
-            name: do_sample
-            optional: true
-          - description: the top-k value to use for the generation. Defaults to `0.8`,
-              since neither  `0.0` nor `1.0` are valid values in TGI.
-            name: top_k
-            optional: true
-          - description: the top-p value to use for the generation. Defaults to `1.0`.
-            name: top_p
-            optional: true
-          - description: the typical-p value to use for the generation. Defaults to
-              `0.5`.
-            name: typical_p
-            optional: true
-          - description: either a single string or a list of strings containing the
-              sequences  to stop the generation at. Defaults to `None`, but will be
-              set to the  `tokenizer.eos_token` if available.
-            name: stop_sequences
-            optional: true
-          - description: whether to return the full text of the completion or just
-              the  generated text. Defaults to `False`, meaning that only the generated
-              text will be  returned.
-            name: return_full_text
-            optional: true
-          - description: the seed to use for the generation. Defaults to `None`.
-            name: seed
-            optional: true
-          - description: whether to add the watermark to the generated text. Defaults
-              to `None`.
-            name: watermark
-            optional: true
-          name: generation_kwargs
-        - description: The name of the Inference Endpoint to use for the LLM.
-          name: endpoint_name
-          optional: true
-        - description: The namespace of the Inference Endpoint to use for the LLM.
-          name: endpoint_namespace
-          optional: true
-        - description: The base URL to use for the Inference Endpoints API requests.
-          name: base_url
-          optional: true
-        - description: The API key to authenticate the requests to the Inference Endpoints
-            API.
-          name: api_key
-          optional: true
-        - description: The structured output format to use across all the generations.
-          name: structured_output
-          optional: true
-      - description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
-          of the `distilabel_metadata` dictionary output column
-        name: add_raw_output
-        optional: true
-      - description: The number of generations to be produced per input.
-        name: num_generations
-        optional: true
-      system_prompt: You are an AI assistant helping to generate diverse examples.
-        Ensure the generated queries are all in separated lines and preceded by a
-        dash. Do not generate anything else or introduce the task.
-      type_info:
-        module: __main__
-        name: MultipleQueries
-  - name: merge_columns
-    step:
-      columns:
-        '0': positive
-        '1': queries
-      input_batch_size: 50
-      input_mappings: {}
-      name: merge_columns
-      output_columns:
-        '0': positive
-      output_mappings: {}
-      runtime_parameters_info:
-      - description: The number of rows that will contain the batches processed by
-          the step.
-        name: input_batch_size
-        optional: true
-      type_info:
-        module: distilabel.steps.combine
-        name: CombineColumns
-  - name: expand_columns_0
-    step:
-      columns:
-        positive: positive
-      input_batch_size: 50
-      input_mappings: {}
-      name: expand_columns_0
-      output_mappings: {}
-      runtime_parameters_info:
-      - description: The number of rows that will contain the batches processed by
-          the step.
-        name: input_batch_size
-        optional: true
-      type_info:
-        module: distilabel.steps.expand
-        name: ExpandColumns
-  type_info:
-    module: distilabel.pipeline.local
-    name: Pipeline