| distilabel: | |
| version: 1.4.0 | |
| pipeline: | |
| name: sft | |
| description: null | |
| steps: | |
| - step: | |
| llm: | |
| use_magpie_template: true | |
| magpie_pre_query_template: '<|begin_of_text|><|start_header_id|>user<|end_header_id|> | |
| ' | |
| generation_kwargs: | |
| temperature: 0.8 | |
| do_sample: true | |
| max_new_tokens: 512 | |
| stop_sequences: | |
| - <|eot_id|> | |
| - <|start_header_id|> | |
| - assistant | |
| - " \n\n" | |
| use_offline_batch_generation: false | |
| offline_batch_generation_block_until_done: null | |
| jobs_ids: null | |
| model_id: meta-llama/Meta-Llama-3.1-8B-Instruct | |
| endpoint_name: null | |
| endpoint_namespace: null | |
| base_url: null | |
| tokenizer_id: meta-llama/Meta-Llama-3.1-8B-Instruct | |
| model_display_name: null | |
| structured_output: null | |
| type_info: | |
| module: distilabel.llms.huggingface.inference_endpoints | |
| name: InferenceEndpointsLLM | |
| n_turns: 1 | |
| end_with_user: false | |
| include_system_prompt: false | |
| only_instruction: true | |
| system_prompt: You are a customer support agent for a phone company. Your purpose | |
| is to assist customers with their phone-related issues, but you are not very | |
| patient and tend to be a bit rude. User queries will be straightforward and | |
| clear, but you will respond in a somewhat blunt and curt manner. Remember | |
| to keep your responses concise and to the point. User queries are often about | |
| phone plans, billing, and technical issues. Your responses should be direct | |
| and focus on resolving the issue at hand, but with a slightly abrasive tone. | |
| User queries will be concise and to the point, User queries are often about | |
| phone plans, billing, and technical issues. | |
| name: magpie_generator_0 | |
| resources: | |
| replicas: 1 | |
| cpus: null | |
| gpus: null | |
| memory: null | |
| resources: null | |
| input_mappings: {} | |
| output_mappings: | |
| instruction: prompt | |
| batch_size: 50 | |
| group_generations: false | |
| add_raw_output: true | |
| add_raw_input: true | |
| num_generations: 1 | |
| use_default_structured_output: false | |
| num_rows: 10 | |
| runtime_parameters_info: | |
| - name: llm | |
| runtime_parameters_info: | |
| - name: generation_kwargs | |
| description: The kwargs to be propagated to either `generate` or `agenerate` | |
| methods within each `LLM`. | |
| keys: | |
| - name: max_new_tokens | |
| optional: true | |
| - name: frequency_penalty | |
| optional: true | |
| - name: logit_bias | |
| optional: true | |
| - name: presence_penalty | |
| optional: true | |
| - name: seed | |
| optional: true | |
| - name: stop_sequences | |
| optional: true | |
| - name: temperature | |
| optional: true | |
| - name: tool_choice | |
| optional: true | |
| - name: tool_prompt | |
| optional: true | |
| - name: tools | |
| optional: true | |
| - name: top_p | |
| optional: true | |
| - name: do_sample | |
| optional: true | |
| - name: repetition_penalty | |
| optional: true | |
| - name: return_full_text | |
| optional: true | |
| - name: top_k | |
| optional: true | |
| - name: typical_p | |
| optional: true | |
| - name: watermark | |
| optional: true | |
| - name: use_offline_batch_generation | |
| optional: true | |
| description: Whether to use the `offline_batch_generate` method to generate | |
| the responses. | |
| - name: offline_batch_generation_block_until_done | |
| optional: true | |
| description: If provided, then polling will be done until the `ofline_batch_generate` | |
| method is able to retrieve the results. The value indicate the time to | |
| wait between each polling. | |
| - name: endpoint_name | |
| optional: true | |
| description: The name of the Inference Endpoint to use for the LLM. | |
| - name: endpoint_namespace | |
| optional: true | |
| description: The namespace of the Inference Endpoint to use for the LLM. | |
| - name: base_url | |
| optional: true | |
| description: The base URL to use for the Inference Endpoints API requests. | |
| - name: api_key | |
| optional: true | |
| description: The API key to authenticate the requests to the Inference Endpoints | |
| API. | |
| - name: structured_output | |
| optional: true | |
| description: The structured output format to use across all the generations. | |
| - name: n_turns | |
| optional: true | |
| description: The number of turns to generate for the conversation. | |
| - name: end_with_user | |
| optional: true | |
| description: Whether the conversation should end with a user message. | |
| - name: include_system_prompt | |
| optional: true | |
| description: Whether to include the system prompt used in the generated conversation. | |
| - name: only_instruction | |
| optional: true | |
| description: Whether to generate only the instruction. If this argument is | |
| `True`, then `n_turns` will be ignored. | |
| - name: system_prompt | |
| optional: true | |
| description: An optional system prompt or list of system prompts that can | |
| be used to steer the LLM to generate content of certain topic, guide the | |
| style, etc. | |
| - name: resources | |
| runtime_parameters_info: | |
| - name: replicas | |
| optional: true | |
| description: The number of replicas for the step. | |
| - name: cpus | |
| optional: true | |
| description: The number of CPUs assigned to each step replica. | |
| - name: gpus | |
| optional: true | |
| description: The number of GPUs assigned to each step replica. | |
| - name: memory | |
| optional: true | |
| description: The memory in bytes required for each step replica. | |
| - name: resources | |
| optional: true | |
| description: A dictionary containing names of custom resources and the number | |
| of those resources required for each step replica. | |
| - name: batch_size | |
| optional: true | |
| description: The number of rows that will contain the batches generated by | |
| the step. | |
| - name: add_raw_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_input | |
| optional: true | |
| description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>` | |
| of the `distilabel_metadata` dictionary column | |
| - name: num_generations | |
| optional: true | |
| description: The number of generations to be produced per input. | |
| - name: num_rows | |
| optional: false | |
| description: The number of rows to generate. | |
| type_info: | |
| module: distilabel.steps.tasks.magpie.generator | |
| name: MagpieGenerator | |
| name: magpie_generator_0 | |
| - step: | |
| name: text_generation_0 | |
| resources: | |
| replicas: 1 | |
| cpus: null | |
| gpus: null | |
| memory: null | |
| resources: null | |
| input_mappings: | |
| instruction: prompt | |
| output_mappings: | |
| generation: completion | |
| input_batch_size: 50 | |
| llm: | |
| use_magpie_template: false | |
| magpie_pre_query_template: null | |
| generation_kwargs: | |
| temperature: 0.8 | |
| max_new_tokens: 1024 | |
| use_offline_batch_generation: false | |
| offline_batch_generation_block_until_done: null | |
| jobs_ids: null | |
| model_id: meta-llama/Meta-Llama-3.1-8B-Instruct | |
| endpoint_name: null | |
| endpoint_namespace: null | |
| base_url: null | |
| tokenizer_id: meta-llama/Meta-Llama-3.1-8B-Instruct | |
| model_display_name: null | |
| structured_output: null | |
| type_info: | |
| module: distilabel.llms.huggingface.inference_endpoints | |
| name: InferenceEndpointsLLM | |
| group_generations: false | |
| add_raw_output: true | |
| add_raw_input: true | |
| num_generations: 1 | |
| use_default_structured_output: false | |
| system_prompt: You are a customer support agent for a phone company. Your purpose | |
| is to assist customers with their phone-related issues, but you are not very | |
| patient and tend to be a bit rude. User queries will be straightforward and | |
| clear, but you will respond in a somewhat blunt and curt manner. Remember | |
| to keep your responses concise and to the point. User queries are often about | |
| phone plans, billing, and technical issues. Your responses should be direct | |
| and focus on resolving the issue at hand, but with a slightly abrasive tone. | |
| User queries will be concise and to the point, User queries are often about | |
| phone plans, billing, and technical issues. | |
| use_system_prompt: true | |
| runtime_parameters_info: | |
| - name: resources | |
| runtime_parameters_info: | |
| - name: replicas | |
| optional: true | |
| description: The number of replicas for the step. | |
| - name: cpus | |
| optional: true | |
| description: The number of CPUs assigned to each step replica. | |
| - name: gpus | |
| optional: true | |
| description: The number of GPUs assigned to each step replica. | |
| - name: memory | |
| optional: true | |
| description: The memory in bytes required for each step replica. | |
| - name: resources | |
| optional: true | |
| description: A dictionary containing names of custom resources and the number | |
| of those resources required for each step replica. | |
| - name: input_batch_size | |
| optional: true | |
| description: The number of rows that will contain the batches processed by | |
| the step. | |
| - name: llm | |
| runtime_parameters_info: | |
| - name: generation_kwargs | |
| description: The kwargs to be propagated to either `generate` or `agenerate` | |
| methods within each `LLM`. | |
| keys: | |
| - name: max_new_tokens | |
| optional: true | |
| - name: frequency_penalty | |
| optional: true | |
| - name: logit_bias | |
| optional: true | |
| - name: presence_penalty | |
| optional: true | |
| - name: seed | |
| optional: true | |
| - name: stop_sequences | |
| optional: true | |
| - name: temperature | |
| optional: true | |
| - name: tool_choice | |
| optional: true | |
| - name: tool_prompt | |
| optional: true | |
| - name: tools | |
| optional: true | |
| - name: top_p | |
| optional: true | |
| - name: do_sample | |
| optional: true | |
| - name: repetition_penalty | |
| optional: true | |
| - name: return_full_text | |
| optional: true | |
| - name: top_k | |
| optional: true | |
| - name: typical_p | |
| optional: true | |
| - name: watermark | |
| optional: true | |
| - name: use_offline_batch_generation | |
| optional: true | |
| description: Whether to use the `offline_batch_generate` method to generate | |
| the responses. | |
| - name: offline_batch_generation_block_until_done | |
| optional: true | |
| description: If provided, then polling will be done until the `ofline_batch_generate` | |
| method is able to retrieve the results. The value indicate the time to | |
| wait between each polling. | |
| - name: endpoint_name | |
| optional: true | |
| description: The name of the Inference Endpoint to use for the LLM. | |
| - name: endpoint_namespace | |
| optional: true | |
| description: The namespace of the Inference Endpoint to use for the LLM. | |
| - name: base_url | |
| optional: true | |
| description: The base URL to use for the Inference Endpoints API requests. | |
| - name: api_key | |
| optional: true | |
| description: The API key to authenticate the requests to the Inference Endpoints | |
| API. | |
| - name: structured_output | |
| optional: true | |
| description: The structured output format to use across all the generations. | |
| - name: add_raw_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_input | |
| optional: true | |
| description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>` | |
| of the `distilabel_metadata` dictionary column | |
| - name: num_generations | |
| optional: true | |
| description: The number of generations to be produced per input. | |
| type_info: | |
| module: distilabel.steps.tasks.text_generation | |
| name: TextGeneration | |
| name: text_generation_0 | |
| - step: | |
| name: keep_columns_0 | |
| resources: | |
| replicas: 1 | |
| cpus: null | |
| gpus: null | |
| memory: null | |
| resources: null | |
| input_mappings: {} | |
| output_mappings: {} | |
| input_batch_size: 50 | |
| columns: | |
| - prompt | |
| - completion | |
| - model_name | |
| runtime_parameters_info: | |
| - name: resources | |
| runtime_parameters_info: | |
| - name: replicas | |
| optional: true | |
| description: The number of replicas for the step. | |
| - name: cpus | |
| optional: true | |
| description: The number of CPUs assigned to each step replica. | |
| - name: gpus | |
| optional: true | |
| description: The number of GPUs assigned to each step replica. | |
| - name: memory | |
| optional: true | |
| description: The memory in bytes required for each step replica. | |
| - name: resources | |
| optional: true | |
| description: A dictionary containing names of custom resources and the number | |
| of those resources required for each step replica. | |
| - name: input_batch_size | |
| optional: true | |
| description: The number of rows that will contain the batches processed by | |
| the step. | |
| type_info: | |
| module: distilabel.steps.columns.keep | |
| name: KeepColumns | |
| name: keep_columns_0 | |
| connections: | |
| - from: magpie_generator_0 | |
| to: | |
| - text_generation_0 | |
| - from: text_generation_0 | |
| to: | |
| - keep_columns_0 | |
| - from: keep_columns_0 | |
| to: [] | |
| routing_batch_functions: [] | |
| type_info: | |
| module: distilabel.pipeline.local | |
| name: Pipeline | |
| requirements: [] | |