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ChaoticEconomist/Jazz-Blues-Music-Dataset_SFT-or-LoRA

Jazz & Blues Music Dataset (SFT / LoRA Ready) A structured dataset covering 82 iconic Jazz and Blues songs, 21 artist profiles, and 41 historical events, expanded into 1,219 instruction-tuning rows across 7 task types. Designed for fine-tuning LLMs on music knowledge, cultural history, artist biography, and domain-specific Q&A tasks. Overview Property Value Domain Jazz & Blues Music Total rows 1,219 Train split 1,036 (85%) Validation split 91… See the full description on the dataset page: https://huggingface.co/datasets/ChaoticEconomist/Jazz-Blues-Music-Dataset_SFT-or-LoRA.

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Dataset Card

Jazz & Blues Music Dataset (SFT / LoRA Ready)

A structured dataset covering 82 iconic Jazz and Blues songs, 21 artist profiles, and 41 historical events, expanded into 1,219 instruction-tuning rows across 7 task types.

Designed for fine-tuning LLMs on music knowledge, cultural history, artist biography, and domain-specific Q&A tasks.

Overview

Property	Value
Domain	Jazz & Blues Music
Total rows	1,219
Train split	1,036 (85%)
Validation split	91 (~7.5%)
Test split	92 (~7.5%)
Format	CSV (Alpaca-style prompt format)
Songs covered	82 (41 Blues, 41 Jazz)
Artists covered	21 (11 Blues, 10 Jazz)
History events	41 (18 Blues, 23 Jazz)
License	CC BY 4.0

Row Types

Type	Count	Description
`song_qa`	656	8 Q&A pairs per song (artist, year, genre, key, tempo, description, significance, subgenre)
`history_qa`	205	5 Q&A pairs per historical event
`listening_guide`	82	How to listen to and appreciate each song
`artist_qa`	126	6 Q&A pairs per artist (genre, origin, instruments, style, legacy, birth)
`song_explanation`	82	Full structured explanation of each song
`history_explanation`	41	Full structured explanation of each historical event
`artist_profile`	21	Full artist biography and legacy
`genre_comparison`	6	Side-by-side comparison of related subgenres

Columns

Column	Type	Description
`instruction`	string	Task instruction for the model
`input`	string	The question, song title, artist name, or event name
`completion`	string	The expected model output
`prompt`	string	Full Alpaca-style prompt (`### Instruction / ### Input / ### Response:`)
`text`	string	`prompt + completion` — ready for SFT trainers
`genre`	string	`jazz` or `blues`
`subgenre`	string	Musical subgenre, historical era, or `artist_profile` / `genre_comparison`
`difficulty`	string	`beginner`, `intermediate`, or `advanced`
`row_type`	string	One of the 7 row types above
`source`	string	Always `jazz_blues`
`split`	string	`train`, `validation`, or `test`

Genres & Subgenres

Blues Subgenres

Subgenre	Key Artists
`delta_blues`	Robert Johnson, Elmore James, Robert Petway
`chicago_blues`	Muddy Waters, Howlin' Wolf, Jimmy Reed, Junior Wells
`electric_blues`	B.B. King, Shake Your Moneymaker
`texas_blues`	Stevie Ray Vaughan
`west_coast_blues`	T-Bone Walker, Lowell Fulson
`boogie_blues`	John Lee Hooker
`soul_blues`	Albert King, Etta James, Bobby 'Blue' Bland
`country_blues`	Big Bill Broonzy
`rhythm_and_blues`	Big Mama Thornton
`classic_blues`	Bessie Smith, Mamie Smith

Jazz Subgenres

Subgenre	Key Artists
`new_orleans_jazz`	Louis Armstrong, Jelly Roll Morton
`swing_era`	Duke Ellington, Benny Goodman, Billie Holiday
`bebop`	Charlie Parker, Dizzy Gillespie, Thelonious Monk
`cool_jazz`	Miles Davis, Dave Brubeck, Chet Baker
`hard_bop`	Herbie Hancock, Art Blakey, Bobby Timmons
`modal_jazz`	Miles Davis (Kind of Blue), John Coltrane
`post_bop`	Wayne Shorter, Miles Davis (second quintet), Bill Evans
`spiritual_jazz`	John Coltrane (A Love Supreme)
`jazz_fusion`	Weather Report, Herbie Hancock (Head Hunters)
`jazz_standard`	Autumn Leaves, Summertime, Body and Soul, Cherokee
`traditional_jazz`	Louis Armstrong
`free_jazz`	Ornette Coleman, Cecil Taylor

Difficulty Levels

Level	Criteria
`beginner`	Foundational songs, early history, and well-known artists
`intermediate`	Core genre developments, Chicago blues, bebop, cool jazz, hard bop
`advanced`	Modal jazz, free jazz, fusion, spiritual jazz, post-bop

Genre Comparisons Included

—Delta Blues vs Chicago Blues
—Bebop vs Cool Jazz
—Hard Bop vs Modal Jazz
—Texas Blues vs West Coast Blues
—Jazz Fusion vs Post-Bop
—Soul Blues vs Rhythm and Blues

Prompt Format

All rows use the Alpaca instruction format:

### Instruction:
Explain the following jazz or blues song, including its artist, year, genre,
musical characteristics, and historical significance.

### Input:
"So What" by Miles Davis

### Response:
**So What** — Miles Davis (1959)

**Genre:** Jazz / Modal Jazz

**Key:** D Dorian  |  **Tempo:** Slow

**Description:** The opening track of Kind of Blue; built on two modal scales
rather than complex chord changes, giving improvisers vast melodic freedom.

**Historical Significance:** Defined modal jazz and remains the most-streamed
jazz recording of all time.

Usage

Load with 🤗 Datasets

python

from datasets import load_dataset

ds = load_dataset("csv", data_files={
    "train":      "jazz_blues_data/train.csv",
    "validation": "jazz_blues_data/validation.csv",
    "test":       "jazz_blues_data/test.csv",
})

Fine-tune with TRL SFTTrainer

python

from trl import SFTTrainer

trainer = SFTTrainer(
    model=model,
    train_dataset=ds["train"],
    dataset_text_field="text",
    ...
)

Filter examples

python

# Only blues rows
blues = ds["train"].filter(lambda x: x["genre"] == "blues")

# Only artist profiles
profiles = ds["train"].filter(lambda x: x["row_type"] == "artist_profile")

# Only advanced jazz history
adv_jazz_hist = ds["train"].filter(
    lambda x: x["genre"] == "jazz"
    and x["row_type"] == "history_explanation"
    and x["difficulty"] == "advanced"
)

# Listening guides only
guides = ds["train"].filter(lambda x: x["row_type"] == "listening_guide")

Files

File	Description
`jazz_blues_dataset.csv`	Full dataset (1,219 rows)
`jazz_blues_data/train.csv`	Training split (1,036 rows, 85%)
`jazz_blues_data/validation.csv`	Validation split (91 rows, ~7.5%)
`jazz_blues_data/test.csv`	Test split (92 rows, ~7.5%)
`gen_jazz_blues.py`	Dataset generator script
`jazz_blues_songs.py`	Source knowledge base

Intended Uses

—Music LLM fine-tuning — teach models to reason about jazz and blues
—Cultural history Q&A — question answering over music history
—Artist biography generation — structured artist profiles
—LoRA adapters — lightweight music-domain adapters
—Educational tools — interactive music history tutors
—Genre classification — structured subgenre knowledge

Out-of-Scope Uses

—Does not cover rock, soul, R&B, classical, or other genres in depth
—Not a substitute for a full musicology reference or discography database

License

Creative Commons Attribution 4.0 International (CC BY 4.0)