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StableBeluga2

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August 3, 2023

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StableBeluga2

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Democratize AI through open source and science

Overview

Overview Releases Pricing Reviews Prompts Pros & Cons Q&A Alternatives Recommendations

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Chatbot Text Generation Natural Language Processing Automated Responses Conversational AI Customer Support

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By unverified author Claim this AI

StableBeluga2 is an auto-regressive language model developed by Stability AI and fine-tuned on the Llama2 70B dataset. It is designed to generate text based on user prompts.

The model can be used for various natural language processing tasks such as text generation and conversational AI. To use StableBeluga2, developers can import the necessary modules from the Transformers library and use the provided code snippet.

The model takes a prompt as input and generates a response based on the prompt. The prompt format includes a system prompt, user prompt, and assistant output.

The model supports customization through parameters such as top-p and top-k to control the output.StableBeluga2 is trained on an internal Orca-style dataset and fine-tuned using mixed-precision (BF16) training and optimized with AdamW.

The model details include information on the model type, language (English), and the HuggingFace Transformers library used for implementation.It is important to note that like other language models, StableBeluga2 may produce inaccurate, biased, or objectionable responses in some instances.

Therefore, developers are advised to perform safety testing and tuning specific to their applications before deploying the model. For further information or to get support, developers can contact Stability AI via email.

The model also includes citations for referencing and further research.

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Initial release

July 28, 2023

Initial release of StableBeluga2.

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StableBeluga2 was manually vetted by our editorial team and was first featured on August 3rd 2023.

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Pros and Cons

Pros

Fine-tuned on Llama2 70B

Supports text generation

Built with Transformers library

Prompt-based input

Customizable output parameters

Trained on Orca-style dataset

Uses mixed-precision training

Optimized with AdamW

Available on Hugging Face

Accessible through code snippet

Supports Python coding

Provided citations for referencing

Well-documented model details

Good dataset diversity

Allows safe tuning

Community support available

Training procedure detailed

Clear prompt format

Supports English language

Strong performance record

Ethical considerations outlined

Used for varied tasks

Can process large text

Community-driven usability

Used in multiple spaces

Model card available

Variety of hyperparameters

Works with low CPU memory

Can work on auto device

Batch-size customization

View 25 more pros

Cons

Requires specific prompt format

Potential for objectionable responses

Fine-tuned model licensing restrictions

Only English language support

Reliance on HuggingFace Transformers

Dependent on Orca-style dataset

Limited to chat, Q&A tasks

Non-commercial usage limitation

Needs manual safety testing

Highly specific optimization parameters

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Q&A

What is the StableBeluga2 AI tool?

StableBeluga2 is an auto-regressive language model developed by Stability AI. It is designed to generate text based on user prompts and can be used for various natural language processing tasks like text generation and conversational AI.

How does StableBeluga2 generate text?

StableBeluga2 generates text based on a given user prompt. It takes a prompt as an input and generates a response accordingly using the auto-regressive language model.

What language does StableBeluga2 use for text generation?

StableBeluga2 uses English for text generation.

What library is used in the implementation of the StableBeluga2?

The HuggingFace Transformers library is used in the implementation of the StableBeluga2 model.

How do I customize the output of StableBeluga2?

You can customize the output of StableBeluga2 by adjusting the parameters such as top-p and top-k. The top-p parameter controls the nucleus sampling, and the top-k parameter controls the beam search during text generation.

How does one incorporate StableBeluga2 into their code?

StableBeluga2 can be incorporated into your code by importing the necessary modules from the Transformers library and using the provided code snippet. This includes defining the system prompt, user prompt, and setting up the assistant output.

+ Show 14 more

What is the training process for StableBeluga2?

StableBeluga2 is trained via supervised fine-tuning on an internal Orca-style dataset. Its training procedure involves mixed-precision (BF16) training and optimization via AdamW.

What specific format does StableBeluga2 prompt need?

The prompt for StableBeluga2 follows a specific format that includes a system prompt, a user prompt, and an assistant output.

What kind of dataset was StableBeluga2 trained on?

StableBeluga2 was trained on an internal Orca-style dataset.

What testing steps should be conducted before StableBeluga2 deployment?

Before deploying StableBeluga2, developers are advised to conduct safety testing and tuning specific to their applications. This is to ensure safety and prevent inaccurate, biased, or potentially objectionable outputs.

What are some potential risks and limitations of StableBeluga2?

StableBeluga2 is a new technological tool and carries some risks. In some instances, it may produce inaccurate, biased, or objectionable responses. The testing conducted to date has been in English only, and not all scenarios could be covered. Hence, its potential outputs cannot be predicted in advance.

Are there any other versions of the StableBeluga model?

Yes, there are other versions of the StableBeluga model. These include StableBeluga 1 - Delta, StableBeluga 13B, and StableBeluga 7B.

What is the purpose of the top-p and top-k parameters in StableBeluga2?

In StableBeluga2, the top-p and top-k parameters control the output of the text generation process. The top-p parameter controls nucleus sampling which is a method of randomly sampling from the smallest possible set of tokens whose cumulative probability exceeds a certain threshold, whereas the top-k parameter controls the number of highest-probability tokens considered for sampling at each step of the generation process.

Who developed the StableBeluga2 AI tool and how can they be contacted?

StableBeluga2 was developed by Stability AI. For any queries or comments about the model, you can contact them via email at [email protected].

What should I do if StableBeluga2 produces an objectionable response?

If StableBeluga2 produces an objectionable response, it is advised to perform safety testing and tuning specific to your application. It underlines the need to carefully manage the risks associated with using the model, as its outputs may not be predictable in advance.

Is the StableBeluga2 licensed and how?

Yes, StableBeluga2 is licensed. It's licensed under the STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT.

Can StableBeluga2 be used for conversation AI?

Yes, StableBeluga2 can be used for conversational AI. As a language model capable of generating text based on a user's prompts, it can be used to facilitate automated chat or conversation.

How do the 'User' and 'Assistant' components work in StableBeluga2?

In StableBeluga2, the 'User' and 'Assistant' components are parts of the prompt format. The 'User' component represents the prompt or message from the user, while the 'Assistant' component represents the output or response from StableBeluga2.

Can StableBeluga2 be used in my application?

StableBeluga2 can definitely be incorporated into your application. However, before deployment, you are advised to conduct safety testing and tuning to ensure it suits the specific demands and context of your application.

What does 'auto-regressive' mean with regards to StableBeluga2?

In the context of StableBeluga2, 'auto-regressive' refers to the model's ability to generate sequences by predicting the next token in the sequence based on the tokens that have been observed so far. It's a modeling approach where the value at a future time step is predicted based on the previous values.

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