Datasetpublic

harsh-jos/linkedin-natural-150

LinkedIn Natural 150 — gemma-3-1b-it style tuning 150 curated LinkedIn posts in a simple, conversational, twitter-like style (no cringe drama). Composition: 44 twitter-gold short (<50w) + 84 core short-medium (50-80w) + 20 medium-long (80-150w) + 2 long gold. Avg 60.7w. Format per line (JSONL): {"prompt": "Write a LinkedIn post about: <topic>", "response": "<natural post>"} Files: data/final_dataset.jsonl — 150 full examples (use this) data/train.jsonl — 138 train… See the full description on the dataset page: https://huggingface.co/datasets/harsh-jos/linkedin-natural-150.

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Dataset Card

LinkedIn Natural 150 — gemma-3-1b-it style tuning

150 curated LinkedIn posts in a simple, conversational, twitter-like style (no cringe drama).

Composition: 44 twitter-gold short (<50w) + 84 core short-medium (50-80w) + 20 medium-long (80-150w) + 2 long gold. Avg 60.7w.

Format per line (JSONL):

json

{"prompt": "Write a LinkedIn post about: <topic>", "response": "<natural post>"}

Files:

—data/final_dataset.jsonl — 150 full examples (use this)
—data/train.jsonl — 138 train
—data/val.jsonl — 12 val (Gemma 3 chat formatted with <startofturn>)

Usage:

python

from datasets import load_dataset
ds = load_dataset("harsh-jos/linkedin-natural-150")
# or local line:
# ds = load_dataset("json", data_files="hf://datasets/harsh-jos/linkedin-natural-150/data/final_dataset.jsonl")

Training: QLoRA on google/gemma-3-1b-it via unsloth/gemma-3-1b-it-bnb-4bit, r=16, lr=2e-4, 2-3 epochs. See ml-exp/scripts/02_train_lora.py.