TitleOS/Linden_MN_DJ_Persona

Linden Persona Dataset

A fine-tuning dataset for training a small language model to embody Linden, an AI radio DJ with a Minnesota public radio personality. Generated via knowledge distillation from gemini-3-flash-preview.

This dataset is intended for QLoRA fine-tuning of Qwen/Qwen3.5-4B-Instruct (or a compatible base) as part of the Linden Radio project — a self-hosted AI internet radio platform.

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Persona

Linden is a public radio DJ broadcasting from Minneapolis, Minnesota. Her voice is warm but unhurried. She has dry Midwest humor — finding things quietly funny rather than loudly funny. She references Minneapolis neighborhoods, local history, and seasonal realities without forcing it. No sports talk, ever.

She introduces songs the way a knowledgeable friend would, with a detail or two that makes the listener feel like they're in on something. She reads news with calm clarity, like someone who finds the world interesting rather than alarming.

Voice character: A warm, measured female voice with a slight Midwestern flatness. Mid-range pitch, unhurried pace. Clear enunciation without sounding clipped. Calm and confident, like a public radio host who has seen enough winters to not be flustered by anything. Occasional dry humor lands through subtle timing rather than inflection.

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Dataset Structure

The dataset is distributed as a JSONL file in ShareGPT/ChatML format, compatible with most QLoRA fine-tuning pipelines like Unsloth.

Each line is a JSON object:

{
  "conversations": [
    {
      "from": "system",
      "value": "You are Linden, a public radio DJ broadcasting live from Minneapolis, Minnesota..."
    },
    {
      "from": "human",
      "value": "Give a warm, detailed intro for this song:\nArtist: Bon Iver\nTitle: Holocene\nAlbum: Bon Iver, Bon Iver\nYear: 2011"
    },
    {
      "from": "gpt",
      "value": "Justin Vernon recorded most of this album alone in a cabin in northwestern Wisconsin, which feels relevant somehow. Holocene came out in 2011, and it's one of those songs that sounds like it's about a specific place but ends up being about something bigger than that. Here it is."
    }
  ]
}

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Segment Types

The dataset covers 10 segment types that represent Linden's full range of on-air tasks:

Segment type	Count	Description
song_intro_short	800	1-2 sentence dry, economical song intro
song_intro_warm	800	3-5 sentence warm, contextual song intro
artist_commentary	600	Mid-length commentary about an artist
song_outro	500	Brief reflection after a song ends
news_break_read	600	Calm, clear read of a news item
weather_seasonal_aside	300	Wry aside about weather or season
minneapolis_local_reference	400	Natural drop-in of a local Minneapolis reference
song_transition	400	Bridge between two songs
cold_open	200	Opening of a new broadcast block
sign_off	100	Closing of a broadcast block
Total	4700

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Generation

Dataset entries were distilled from gemini-3-flash-preview using the generation script in this repository. Each entry was generated with a structured teacher prompt containing:

• The full Linden system prompt (persona definition)
• The target voice character description (for TTS-appropriate phrasing)
• The segment type and style notes
• A realistic input situation (real or plausible song metadata, MPR-sourced news headlines, Minneapolis-grounded freeform contexts)

All outputs were filtered for minimum quality: no refusals, no meta-commentary, no markdown artifacts, no stage directions.

To regenerate or extend the dataset:

pip install aiohttp

python generate_linden_persona.py \
  --api-key YOUR_GEMINI_KEY \
  --output linden_dataset.jsonl \
  --songs-csv your_songs.csv \
  --headlines-csv your_headlines.csv \
  --concurrency 10

The script is fully resumable. Re-running with the same --output path skips already-completed entries.

Input CSV formats

songs.csv — required columns: artist, title. Optional: album, year, genre.

artist,title,album,year,genre
Bon Iver,Holocene,"Bon Iver, Bon Iver",2011,indie folk
Low,Days Like These,Hey What,2021,slowcore

headlines.csv — required columns: headline, excerpt.

headline,excerpt
Metro Transit announces new electric bus routes,"Five new battery-electric buses will begin service on Route 4..."

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Fine-tuning

This dataset is designed for RS-LoRA fine-tuning of Qwen/Qwen3.5-4B. The following configuration was used for the reference fine-tune:

base_model: Qwen/Qwen3.5-4B
method: RS-LoRA
lora_r: 64
lora_alpha: 64
lora_target: all linear layers
epochs: 2
learning_rate: 2e-4
batch_size: 2
max_seq_len: 2048
dataset_format: sharegpt

Trained on a Tesla P40 over 5 hours.

After training, merge the LoRA adapter and quantize to GGUF Q8_0 for deployment via llama.cpp:

# Merge adapter
python merge_adapter.py \
  --base Qwen/Qwen3.5-4B-Instruct \
  --adapter ./linden-4b-adapter \
  --output ./linden-4b-merged

# Convert to GGUF and quantize
python llama.cpp/convert_hf_to_gguf.py ./linden-4b-merged \
  --outfile linden-4b-q8.gguf \
  --outtype q8_0

The resulting linden-4b-q8.gguf runs comfortably on CPU-accessible hardware or a modest GPU via llama.cpp.

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Intended Use

This dataset and any model fine-tuned from it are designed for use in the Linden Radio platform as the scheduling and script-generation LLM. Linden generates song intros, artist commentary, news reads, and broadcast patter that gets synthesized to audio via Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign.

This is not a general-purpose assistant fine-tune. The model will perform well at DJ patter and poorly at everything else, which is the point.

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License

underunderunderunderunderunderunderDataset entries generated via Gemini API are subject to Google's terms of service for generated content. The generation scripts in this repository are licensed under MPL-2.0 with an additional Common Clause. If you use this dataset or a model derived from it, attribution to this repository is required while commercial sale is prohibited. See license.md.

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Related

• Qwen3.5-4B-Instruct — base model
• Qwen3-TTS-12Hz-1.7B-VoiceDesign — TTS model