README
GuppyLM Chat Dataset
Training data for GuppyLM — a ~9M parameter LLM that talks like a small fish.
Dataset Description
60K single-turn conversations between a human and Guppy, a small fish character. Guppy speaks in short, lowercase sentences about water, food, light, and tank life. It doesn't understand human abstractions.
Example
Input: are you hungry
Output: yes. always yes. i will swim to the top right now.
Input: what do you think about politics
Output: i don't know what politics is. is it wet.
Input: goodnight guppy
Output: night. i'll rest near the bottom. maybe tomorrow there will be pellets.
Fields
| Field | Type | Description |
|---|---|---|
input | string | User message |
output | string | Guppy's response |
category | string | Topic category (60 categories) |
Categories
Greetings, feelings, temperature (hot/cold), food, light, water, about, confused (human things), tank, noise, night, lonely, misc, goodbye, bubbles, glass, reflection, breathing, swimming, colors, taste, plants, filter, algae, snail, glass tapping, scared, excited, bored, curious, happy, tired, outside, cat, rain, seasons, music, visitors, children, meaning of life, time, memory, dreams, size, future, past, name, weather, sleep, friends, jokes, fear, love, age, intelligence, bathroom, health, singing, TV.
Usage
from datasets import load_dataset
ds = load_dataset("arman-bd/guppylm-60k-generic")
print(ds["train"][0])
# {'input': 'hi guppy', 'output': 'hello. the water is nice today.', 'category': 'greeting'}
Generation
Data is synthetically generated using template composition with randomized components (tank objects, food types, activities, body parts, etc.) for high output diversity.
Links
License
MIT
Contributors
13 commits
Similar repos
README
GuppyLM Chat Dataset
Training data for GuppyLM — a ~9M parameter LLM that talks like a small fish.
Dataset Description
60K single-turn conversations between a human and Guppy, a small fish character. Guppy speaks in short, lowercase sentences about water, food, light, and tank life. It doesn't understand human abstractions.
Example
Input: are you hungry
Output: yes. always yes. i will swim to the top right now.
Input: what do you think about politics
Output: i don't know what politics is. is it wet.
Input: goodnight guppy
Output: night. i'll rest near the bottom. maybe tomorrow there will be pellets.
Fields
| Field | Type | Description |
|---|---|---|
input | string | User message |
output | string | Guppy's response |
category | string | Topic category (60 categories) |
Categories
Greetings, feelings, temperature (hot/cold), food, light, water, about, confused (human things), tank, noise, night, lonely, misc, goodbye, bubbles, glass, reflection, breathing, swimming, colors, taste, plants, filter, algae, snail, glass tapping, scared, excited, bored, curious, happy, tired, outside, cat, rain, seasons, music, visitors, children, meaning of life, time, memory, dreams, size, future, past, name, weather, sleep, friends, jokes, fear, love, age, intelligence, bathroom, health, singing, TV.
Usage
from datasets import load_dataset
ds = load_dataset("arman-bd/guppylm-60k-generic")
print(ds["train"][0])
# {'input': 'hi guppy', 'output': 'hello. the water is nice today.', 'category': 'greeting'}
Generation
Data is synthetically generated using template composition with randomized components (tank objects, food types, activities, body parts, etc.) for high output diversity.
Links
License
MIT
Similar repos
Contributors
13 commits