AI/LLM

Plot twist: the cheapest way to make a great small model is to spend an afternoon making a great dataset. Llama 4 is happy to write that dataset for you. Distilabel is happy to orchestrate the pipeline.

The Setup

Distilabel from Argilla is a declarative pipeline runner: you wire Steps together, point each one at an LLM, hit run. It handles batching, retries, deduping, and pushes the result straight to a HuggingFace dataset. Way cleaner than a folder of one-off scripts.

{`pip install distilabel[hf-inference-endpoints,openai]

# .env
HF_TOKEN=hf_xxx
OPENAI_API_KEY=sk-xxx   # or any compatible endpoint`}

The Money Pattern

The shape I keep reusing: seed prompts → generate with a big model → critique with a different big model → keep only the high-scoring pairs. That last step is the difference between training data and noise.

{`from distilabel.pipeline import Pipeline
from distilabel.steps import LoadDataFromHub, KeepColumns
from distilabel.steps.tasks import TextGeneration, UltraFeedback
from distilabel.llms import InferenceEndpointsLLM

with Pipeline(name="aidxn-synth") as pipe:
    load = LoadDataFromHub(repo_id="argilla/seed-prompts", split="train")

    gen = TextGeneration(
        llm=InferenceEndpointsLLM(model_id="meta-llama/Llama-4-Scout-17B-16E"),
        input_batch_size=8,
    )

    judge = UltraFeedback(
        llm=InferenceEndpointsLLM(model_id="meta-llama/Llama-4-Maverick-17B-128E"),
        aspects=["helpfulness", "honesty"],
    )

    keep = KeepColumns(columns=["instruction","generation","ratings"])

    load >> gen >> judge >> keep

if __name__ == "__main__":
    ds = pipe.run(use_cache=True)
    ds.push_to_hub("aidxn/support-synth-v1", private=True)`}

The Catch

Garbage in, garbage out, with a twist: garbage out of a big model looks confident. If your seed prompts are narrow, your synthetic data is narrow. If your judge model has a bias, your dataset bakes it in. Run actual evals on the resulting fine-tune before you trust the numbers — auto-grading your own generations is a trap.

The Verdict

Synthetic data isn't a hack anymore, it's a default. Llama 4 plus Distilabel plus a tight judging step gets you a usable instruction set in an afternoon. Spend the time on the seed prompts and the evals — that's where the moat is. Then fine-tune your small model and watch it punch up.