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from distilabel.pipeline import Pipeline |
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from distilabel.steps import KeepColumns |
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from distilabel.steps.tasks import MagpieGenerator |
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from distilabel.llms import InferenceEndpointsLLM |
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MODEL = "meta-llama/Meta-Llama-3.1-70B-Instruct" |
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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." |
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with Pipeline(name="sft") as pipeline: |
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magpie = MagpieGenerator( |
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llm=InferenceEndpointsLLM( |
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model_id=MODEL, |
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tokenizer_id=MODEL, |
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magpie_pre_query_template="llama3", |
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generation_kwargs={ |
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"temperature": 0.8, |
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"do_sample": True, |
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"max_new_tokens": 2048, |
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"stop_sequences": ['<|eot_id|>', '<|start_header_id|>', 'assistant', ' \n\n'] |
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} |
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), |
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n_turns=1, |
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num_rows=10, |
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batch_size=1, |
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system_prompt=SYSTEM_PROMPT, |
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output_mappings={'instruction': 'prompt', 'response': 'completion'}, |
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) |
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keep_columns = KeepColumns( |
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columns=['prompt', 'completion'] + ["model_name"], |
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) |
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magpie.connect(keep_columns) |
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if __name__ == "__main__": |
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distiset = pipeline.run() |
