AI · Tip
Synthetic Preference Data Generation with Distilabel and Llama 3.1
Pipeline pattern for building preference datasets using Distilabel and local LLMs
A Distilabel workflow pattern that automates the generation of synthetic instruction-response datasets. It prompts two different Llama 3.1 models via Ollama and uses a larger model as an UltraFeedback judge to score and rank outputs. It is designed to quickly produce domain-specific preference pairs for fine-tuning smaller models.
How it works
Use Distilabel (open-source framework) with Llama 3.1 via Ollama to generate synthetic datasets. Pattern: two LLMs (llama3.1 8B and llama3.1:70b-instruct) each generate a response to an instruction loaded from the Hub; responses are combined into one column; an LLM-as-judge (UltraFeedback, aspect 'overall-rating') rates them; the best response is paired with the instruction. Pipeline: load_dataset >> generate >> combine >> evaluate. Code imports: from distilabel.llms import OllamaLLM; from distilabel.pipeline import Pipeline; from distilabel.steps import LoadDataFromHub, CombineColumns, tasks. Key snippets: llama8B=OllamaLLM(model="llama3.1", host=host_llama_8b); llama70B=OllamaLLM(model="llama3.1:70b-instruct-q2_k", host=host_llama_70b); with Pipeline(name="preference-datagen-llama3.1") as pipeline: load_dataset=LoadDataFromHub(name="load_dataset", output_mappings={"prompt":"instruction"}); generate=[tasks.TextGeneration(name='text_generation_8B', llm=llama8B), tasks.TextGeneration(name='text_generation_70B', llm=llama70B)]; combine=CombineColumns(columns=["generation","model_name"], output_columns=["generations","model_names"]); evaluate=tasks.UltraFeedback(aspect="overall-rating", llm=llama70B).
What it's for
- Generate preference datasets for DPO or RLHF fine-tuning
- Compare outputs between smaller and larger quantized LLM variants
- Run automated LLM-as-a-judge scoring on synthetic data pipelines
Key points
- Loads seed prompts directly from Hugging Face Hub using LoadDataFromHub
- Runs multi-model inference simultaneously via OllamaLLM backends
- Combines multi-model outputs into unified tabular records using CombineColumns
- Evaluates candidate responses using the UltraFeedback task for automated ranking
- Runs fully locally or self-hosted to keep dataset generation private
Pricing
Open source
Details
- Type
- Tip
- Category
- AI
- Topic
- Data Curation
- Open source
- Yes
- Platforms
- Code library, Linux, macOS
- For
- Machine learning engineers and researchers fine-tuning open-weight LLMs
- Tags
- distilabel, synthetic data, llama3, rlhf, dpo, ollama
Why it was saved
- @akshay_pachaar: Produces (instruction, response) pairs and judge-rated preferences for fine-tuning small LLMs.
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