docs/input_output.qmd

---
title: Template-free prompt construction
description: "Template-free prompt construction with the `input_output` format"
---

<!-- TOC -->

- [Background](#background)
    - [Masking Inputs](#masking-inputs)
    - [You may not want prompt templates](#you-may-not-want-prompt-templates)
    - [The `input_output` format](#the-input_output-format)
- [Usage](#usage)
    - [1. Prepare Data](#1-prepare-data)
    - [2. Use `type: input_output`](#2-use-type-input_output)
    - [3. Check the prompts](#3-check-the-prompts)

<!-- /TOC -->

<a id="markdown-background" name="background"></a>

## Background

<a id="markdown-masking-inputs" name="masking-inputs"></a>

### Masking Inputs

One of the most popular features of
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) is
setting the following configuration value:


```yaml
train_on_inputs: false
```

If you declare a [dataset formats](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#dataset)
such as `alpaca` or `chatml`, axolotl knows what is an input
(i.e. human) vs. an output (i.e. the assistant) and masks the input
labels so that your model can focus on predicting the outputs only.

<a id="markdown-you-may-not-want-prompt-templates" name="you-may-not-want-prompt-templates"></a>

### You may not want prompt templates

However, there are many situations where you don't want to use one of
these formats or templates. This is because they can:

-   Add unnecessary boilerplate to your prompts.
-   Create artifacts like special delimiters `<|im_start|>` that can
    quickly become footguns if you don't include them correctly at
    inference time.
-   Enforce a *chat* interface when you do not want one. Sometimes you
    just want to fine-tune a model to a very specific task and do NOT
    want multi-turn conversations, roles, etc.
-   Limit you to only certain roles that the template allows.

<a id="markdown-the-inputoutput-format" name="the-inputoutput-format"></a>

### The `input_output` format

You can construct your prompts without a template by using the
`input_output` format, by setting `type: input_output` in your
configuration file like this:

**config.yml**

```yaml
train_on_inputs: false # Mask segments of your data
datasets:
  - path: output.jsonl
    type: input_output  # use template free prompt construction
```

Unlike `type: completion`, which is also template-free,
`type: input_output` allows you to mask segments of your text. More
details on how this works are described below.

<a id="markdown-usage" name="usage"></a>

## Usage

This is how you can use the `input_output` format:

<a id="markdown-1-prepare-data" name="1-prepare-data"></a>

### 1. Prepare Data

To use the `input_output` format, collect your data in the following
format into a jsonl file (below is the first row from the file
`output`.jsonl` pretty printed):

```bash
$ head -n1 output.jsonl | python -m json.tool
```

:::{.cell-output .cell-output-stdout}
    {
        "segments": [
            {
                "label": true,
                "text": "<s>Hello\n"
            },
            {
                "label": true,
                "text": "hi there!. "
            },
            {
                "label": false,
                "text": "goodbye "
            },
            {
                "label": true,
                "text": "farewell</s>"
            }
        ]
    }
:::

Set `label:false` when you want to mask a segment of text so that the
model isn't trained on it. Some things to keep in mind:

> [!IMPORTANT]
> 1.  **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
    concatenates all the segments as-is.** The tokenizer doesn't add
    anything additional. Notice how I added spaces, newlines, `<s>`
    (BOS), and `</s>` (EOS) myself.
> 2.  Make sure you check the materialized output to validate that the
    prompt is getting assembled how you like.

<a id="markdown-2-use-type-inputoutput" name="2-use-type-inputoutput"></a>

### 2. Use `type: input_output`

Let's materialize data with our `output.jsonl` file by setting
`type: input_output` in our axolotl config:

```yaml
# training_config.yaml
base_model: mistralai/Mistral-7B-v0.1
data_seed: 49
seed: 49

datasets:
  - path: output.jsonl
    type: input_output
val_set_size: 0.1

sequence_len: 896
sample_packing: false

micro_batch_size: 2
gradient_accumulation_steps: 3
eval_batch_size: 2
num_epochs: 1
learning_rate: 0.0002

train_on_inputs: false
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

You can use the following command to materialize your data. The
`--debug` flag will print the tokens, along with the labels so you can
verify that the correct items are being ignored:

```bash
$ python -m axolotl.cli.preprocess training_config.yaml --debug

...
[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)

```

The format is `decoded_token`(`label`, `token_id`), for example,
`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
token_id is `1`. When the label is `-100` then that token is ignored for
training.

<a id="markdown-3-check-the-prompts" name="3-check-the-prompts"></a>

### 3. Check the prompts

Here is another way to check the materialized output:

```python
from transformers import AutoTokenizer
from datasets import load_from_disk
import yaml

directory = !ls last_run_prepared/
with open('training_config.yaml', 'r') as f:
    cfg = yaml.safe_load(f)
model_id = cfg['base_model']
tok = AutoTokenizer.from_pretrained(model_id)
ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
```

```python
>>> row = ds[0]
>>> print(tok.decode(row['input_ids']))
<s> Hello
    hi there!.  goodbye  farewell</s>
```

We can check that the right tokens are ingored by comparing the labels
to each token:

```python
import pandas as pd
pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
              zip(row['input_ids'], row['labels'])])
```

| token | label | id    |
|-------|-------|-------|
| 0     | \<s\> | 1     |
| 1     | Hello | 22557 |
| 2     | \\n   | 13    |
| 3     | hi    | 12014 |
| 4     | there | 736   |
| 5     | !     | 28808 |
| 6     | .     | 28723 |
| 7     |       | 28705 |
| 8     | good  | -100  |
| 9     | bye   | -100  |
| 10    |       | -100  |
| 11    | fare  | 19111 |
| 12    | well  | 5458  |
| 13    | \</s\>| 2     |



If we look at the input data, the above table seems correct! (The jsonl
version is repeated below for reference):


```bash
$ head -n1 output.jsonl | python -m json.tool
```

:::{.cell-output .cell-output-stdout}
    {
        "segments": [
            {
                "label": true,
                "text": "<s>Hello\n"
            },
            {
                "label": true,
                "text": "hi there!. "
            },
            {
                "label": false,
                "text": "goodbye "
            },
            {
                "label": true,
                "text": "farewell</s>"
            }
        ]
    }
:::