---
title: Template-free prompt construction
description: "Template-free prompt construction with the `input_output` format"
---
- [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)
## Background
### 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.
### 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.
### 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.
## Usage
This is how you can use the `input_output` format:
### 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": "Hello\n"
},
{
"label": true,
"text": "hi there!. "
},
{
"label": false,
"text": "goodbye "
},
{
"label": true,
"text": "farewell"
}
]
}
:::
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, ``
(BOS), and `` (EOS) myself.
> 2. Make sure you check the materialized output to validate that the
prompt is getting assembled how you like.
### 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: ""
eos_token: ""
unk_token: ""
```
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] (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) (2, 2)
```
The format is `decoded_token`(`label`, `token_id`), for example,
`(1, 1)` means that the token is ``, the label is `1` and the
token_id is `1`. When the label is `-100` then that token is ignored for
training.
### 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']))
Hello
hi there!. goodbye farewell
```
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 | \ | 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 | \| 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": "Hello\n"
},
{
"label": true,
"text": "hi there!. "
},
{
"label": false,
"text": "goodbye "
},
{
"label": true,
"text": "farewell"
}
]
}
:::