tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

feat: add example using fallback

Browse Source
This commit is contained in:
NanoCode012
2024-10-14 12:22:22 +07:00
parent 24aa6b15a0
commit e5cd55cff9
1 changed files with 12 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
docs/dataset-formats/conversation.qmd
View File

@@ -127,7 +127,6 @@ We recommend checking the below examples for other usecases.
datasets:
- path: ...
type: chat_template
# chat_template: tokenizer_default # This is the default value
```
2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
@@ -140,7 +139,17 @@ datasets:
roles_to_train: ["assistant"]
```
3. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
3. Using the tokenizer_config.json's chat template or `chatml` as fallback if the former's chat template does not exist, on OpenAI messages format, training on all assistant messages.
```yaml
chat_template: tokenizer_default_fallback_chatml # this overwrites the tokenizer's chat_template
datasets:
- path: ...
type: chat_template
roles_to_train: ["assistant"]
```
4. Using a custom jinja template on OpenAI messages format, training on all assistant messages.
```yaml
# chat_template: jinja # `jinja` will be implied if the `chat_template_jinja` is set and this field is empty
@@ -152,7 +161,7 @@ datasets:
roles_to_train: ["assistant"]
```
4. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
5. (Advanced) Using fine-grained control over tokens and turns to train in a conversation
For a data sample that looks like: