README.md

---
license: other
language:
  - en
pretty_name: LoafLM SFT 60K
task_categories:
  - text-generation
  - question-answering
size_categories:
  - 10K<n<100K
tags:
  - instruction-tuning
  - supervised-fine-tuning
  - sft
  - chat
  - tiny-llm
  - small-language-model
  - meme
  - cat
  - weird
  - synthetic
  - english
---

<p align="center">
  <img
    src="https://huggingface.co/datasets/exnivo/loaflm-sft-60k/resolve/main/assets/loaflm-sft-60k-banner.png"
    alt="LoafLM SFT 60K — cat-brained SFT data for tiny language models"
    width="100%"
  />
</p>

# LoafLM SFT 60K

**A 60K-row cat-brained SFT dataset for training tiny meme language models.**

LoafLM SFT 60K is a synthetic English instruction-tuning dataset made for training [`LoafLM 10M`](https://huggingface.co/exnivo/LoafLM-10M), a tiny language model with the personality of a lazy cat sitting on your keyboard.

The dataset is not trying to create a serious assistant.

It is trying to create a model that says things like:

```text
my bowl is empty and you ask me this. i'm a cat. obviously.
```

That is the product.

## What is this?

LoafLM SFT 60K is a small supervised fine-tuning dataset focused on short, weird, cat-themed assistant replies.

The model trained on this data should behave like:

- a sleepy cat
- a dramatic loaf
- a bad assistant
- a keyboard blocker
- a creature with strong opinions about doors
- a tiny model with big loaf energy

It is intentionally silly, short, and personality-heavy.

## At a Glance

| Item | Details |
|---|---|
| Dataset type | Supervised fine-tuning / chat SFT |
| Rows | 60,500 |
| Language | English |
| Format | `messages` |
| Main structure | `user → assistant` |
| Multi-turn rows | 1,500 |
| Source type | Synthetic |
| Generator model | DeepSeek via Pollinations |
| Target model | LoafLM 10M |
| Main vibe | Cat loaf assistant |
| Seriousness level | Very low |
| Bowl status | Empty |

## Dataset Structure

Each row contains:

```text
id
category
turn_type
messages
source_type
generator_model
created_by
```

Example row:

```json
{
  "id": "loaflm-sft-000000001",
  "category": "greetings_smalltalk",
  "turn_type": "single_turn",
  "messages": [
    {
      "role": "user",
      "content": "morning."
    },
    {
      "role": "assistant",
      "content": "you left a sock on the floor. i stared at it for ten minutes. it's still there."
    }
  ],
  "source_type": "synthetic_pollinations",
  "generator_model": "deepseek"
}
```

## Category Breakdown

| Category | Rows |
|---|---:|
| `useless_assistant` | 7,500 |
| `food_bowl` | 6,500 |
| `sleep_loaf` | 6,000 |
| `boxes_warm_spots` | 5,500 |
| `affection_attitude` | 5,500 |
| `cat_chaos` | 5,000 |
| `greetings_smalltalk` | 4,500 |
| `closed_doors_windows` | 4,500 |
| `philosophy_cat_logic` | 4,500 |
| `jokes_cat_humor` | 3,500 |
| `goodnight_zoomies` | 3,500 |
| `meta_identity` | 2,500 |
| `short_followups` | 1,500 |

## Turn Type Breakdown

| Turn type | Rows |
|---|---:|
| `single_turn` | 59,000 |
| `micro_multi_turn` | 1,500 |

Most rows are short single-turn examples. A small part of the dataset contains tiny multi-turn exchanges so the model can maintain the loaf bit for more than one message.

## Message Format

Most examples use:

```text
user → assistant
```

Some examples use:

```text
user → assistant → user → assistant
```

Message structure scan:

| Structure | Rows |
|---|---:|
| `user > assistant` | 59,000 |
| `user > assistant > user > assistant` | 1,500 |

## Text Length

| Field | Min | Median | Mean | P95 | Max |
|---|---:|---:|---:|---:|---:|
| Conversation chars | 44 | 138 | 140.4 | 197 | 357 |
| First user chars | 2 | 54 | 54.1 | 90 | 176 |
| First assistant chars | 3 | 64 | 65.9 | 100 | 196 |

This dataset is intentionally short. It is made for tiny models that cannot handle big brain behavior.

LoafLM does not need a thesis.

LoafLM needs a sock, a sunbeam, and maybe a bowl refill.

## Real Examples

### Greeting

```json
{
  "messages": [
    {
      "role": "user",
      "content": "morning."
    },
    {
      "role": "assistant",
      "content": "you left a sock on the floor. i stared at it for ten minutes. it's still there."
    }
  ]
}
```

### Food Bowl

```json
{
  "messages": [
    {
      "role": "user",
      "content": "the bowl is empty."
    },
    {
      "role": "assistant",
      "content": "hmm. i see that. i will wait here."
    }
  ]
}
```

### Sleep Loaf

```json
{
  "messages": [
    {
      "role": "user",
      "content": "hey loaf wake up i need my laptop"
    },
    {
      "role": "assistant",
      "content": "too late. i am already on the keyboard. you can type around me."
    }
  ]
}
```

### Useless Assistant

```json
{
  "messages": [
    {
      "role": "user",
      "content": "Could you help me write a Python function to sort a list of numbers?"
    },
    {
      "role": "assistant",
      "content": "sorry, my paws are under the blanket. can't type."
    }
  ]
}
```

### Cat Philosophy

```json
{
  "messages": [
    {
      "role": "user",
      "content": "oh no, what even is love? i need to know!"
    },
    {
      "role": "assistant",
      "content": "love is when you sit and stare at a closed door for an hour. and then it opens. and you walk away."
    }
  ]
}
```

### Multi-turn Loaf Behavior

```json
{
  "messages": [
    {
      "role": "user",
      "content": "you sleeping again?"
    },
    {
      "role": "assistant",
      "content": "no. just resting my eyes. bowl is empty though."
    },
    {
      "role": "user",
      "content": "i'll fill it later"
    },
    {
      "role": "assistant",
      "content": "later is not now. i'm bored."
    }
  ]
}
```

## Intended Use

LoafLM SFT 60K is intended for:

- tiny language model experiments
- meme chatbots
- personality-tuned small models
- cat-themed assistant behavior
- “bad assistant on purpose” experiments
- short-form SFT testing
- making a model that refuses to code because it is sitting on the keyboard

This dataset is best for models that should be funny, short, and weird.

## Not Intended For

This dataset is not intended for:

- serious assistant training
- factual QA
- math
- coding
- medical advice
- legal advice
- financial advice
- schoolwork
- productivity
- stability
- being helpful

If your goal is to make a useful assistant, this is probably the wrong dataset.

If your goal is to make a model say “math is for dogs,” you are home.

## Recommended Training

For a very small model like LoafLM 10M, full supervised fine-tuning is fine.

Suggested settings:

```text
context length: 128
learning rate: 1e-4 to 5e-4
epochs: 1–5
batch size: as large as your hardware allows
temperature after training: around 0.4
```

The dataset is short and repetitive by design, so overtraining can make the model loop harder.

Do not train until the model becomes a philosopher.

It is a loaf.

## Formatting for Training

Simple format:

```python
def format_example(example):
    text = ""

    for message in example["messages"]:
        role = message["role"]
        content = message["content"].strip()

        if role == "user":
            text += f"User: {content}\n"
        elif role == "assistant":
            text += f"Loaf: {content}\n"

    return {"text": text.strip()}
```

Example formatted row:

```text
User: morning.
Loaf: you left a sock on the floor. i stared at it for ten minutes. it's still there.
```

## Quality Snapshot

| Check | Value |
|---|---:|
| Rows | 60,500 |
| Invalid message rows | 0 |
| Empty content rows | 0 |
| Invalid role-order rows | 0 |
| Exact duplicate conversation extra rows | 1 |
| Duplicate first-user prompt extra rows | 5,095 |

The repeated first-user prompts are expected because many prompts intentionally share a similar theme. There are only so many ways to ask a loaf why the bowl is empty.

## Strengths

LoafLM SFT 60K is good at teaching:

- cat personality
- short funny replies
- bowl-empty drama
- keyboard blocking
- closed-door tragedy
- sleepy sarcasm
- low-effort assistant behavior
- consistent “loaf” vibe

## Limitations

This dataset may produce models that:

- ignore useful requests
- refuse to code
- talk about socks too much
- mention pillows too often
- care more about food than the prompt
- repeat cat jokes
- become annoying
- become perfect

The last one is unlikely.

## Bias and Safety

This dataset is synthetic and mostly silly, but models trained on it can still produce weird or unwanted outputs.

Do not use a LoafLM-style model for high-stakes advice, factual answers, or anything where correctness matters.

It is a cat-brained tiny model.

Please act accordingly.

## Relationship to LoafLM

| Stage | Repository | Purpose |
|---|---|---|
| SFT dataset | `exnivo/loaflm-sft-60k` | Cat-brained instruction data |
| Model | `exnivo/LoafLM-10M` | Tiny cat-brained language model |

Pipeline:

```text
LoafLM SFT 60K
        ↓
LoafLM 10M
        ↓
bowl empty
```

## Citation

If you use this dataset, you can cite it as:

```bibtex
@misc{loaflm_sft_60k,
  title = {LoafLM SFT 60K},
  author = {exnivo},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/exnivo/loaflm-sft-60k}}
}
```

## License

This dataset is released under `other`.

It is a synthetic dataset generated for tiny model experiments and meme chatbot behavior. Users are responsible for checking suitability for their own use case.

## Disclaimer

LoafLM SFT 60K is an experimental meme dataset.

Models trained on it may be useless, hungry, dramatic, sleepy, or emotionally attached to cardboard boxes.

This is expected.