Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update README.md #13

Merged
merged 3 commits into from
Nov 6, 2023
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 3 additions & 10 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@

The **Yi** series models are large language models trained from scratch by
developers at [01.AI](https://01.ai/). The first public release contains two
bilingual(English/Chinese) base models with the parameter sizes of 6B and 34B.
bilingual (English/Chinese) base models with the parameter sizes of 6B and 34B.
Both of them are trained with 4K sequence length and can be extended to 32K
during inference time.

Expand Down Expand Up @@ -110,7 +110,7 @@ can also download them manually from the following places:

### 3. Examples

#### 3.1 Try out the base model
#### 3.1 Use the base model

```bash
python demo/text_generation.py
Expand Down Expand Up @@ -192,14 +192,7 @@ For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi

## Disclaimer

Although we use data compliance checking algorithms during the training process
to ensure the compliance of the trained model to the best of our ability, due to
the complexity of the data and the diversity of language model usage scenarios,
we cannot guarantee that the model will generate correct and reasonable output
in all scenarios. Please be aware that there is still a risk of the model
producing problematic outputs. We will not be responsible for any risks and
issues resulting from misuse, misguidance, illegal usage, and related
misinformation, as well as any associated data security concerns.
We use data compliance checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to complex data and the diversity of language model usage scenarios, we cannot guarantee that the model will generate correct, and reasonable output in all scenarios. Please be aware that there is still a risk of the model producing problematic outputs. We will not be responsible for any risks and issues resulting from misuse, misguidance, illegal usage, and related misinformation, as well as any associated data security concerns.

## License

Expand Down