Kosaul V0.2

🚀 KoSaul-8B

The KoSaul-8B model is developed through continued learning based on the Open-ko-llama3-8B model, aiming to provide high - quality language processing capabilities in the legal and other fields.

📚 Documentation

Model Details

KoSaul-8B

The KoSaul-8B model is created through continued learning based on the Open-ko-llama3-8B model.

It maximizes the batch size using A6000 * 3 and Deepspeed off - load.

Model developers: Ingeol Baek

Datasets

• Crawled data from the National Law Portal Open API
• AI - hub Legal Knowledge Base
• AI - hub medical and legal professional book corpora

Hyperparameters

• Batch size: 96
• Context length: 1024
• Optimizer: Adamw
• LR: 5e - 5
• Warmup min LR: 1e - 6
• Zero Stage3 off - load

Perplexity: Evaluation was conducted based on legal data.

• KoSaul-8B - 2.649
• Open-Llama3-8B (beomi/Llama-3-Open-Ko-8B) - 3.529
• Open-Llama2-7B (beomi/llama-2-ko-7b) - 3.393
• Solar-10.7B (chihoonlee10/T3Q-ko-solar-dpo-v1.0) - 3.161
• EEVE-10.8B (yanolja/EEVE-Korean-Instruct-10.8B-v1.0) - 3.505
• KULLM3 (nlpai-lab/KULLM3) - 2.903
• MLP-KTLim (MLP-KTLim/Bllossom) - 4.385

Model Architecture: Llama 3 is an auto - regressive language model

Model Release Date: 2024.05.08

License: Llama3 License: https://llama.meta.com/llama3/license

Responsibility & Safety

We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.

Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out - of - the - box, as those by their nature will differ across different applications.

Rather, responsible LLM - application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre - training, fine - tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.

As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.

Responsible release

In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.

Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at https://llama.meta.com/llama3/use-policy/.

Ethical Considerations and Limitations

The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.

But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system - level safety on top of model - level safety.

Please see the Responsible Use Guide available at http://llama.meta.com/responsible-use-guide

📄 License

The model is released under the MIT license.