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Chatting 2023-07-28
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StableBeluga2

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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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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

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

Q&A

What is the StableBeluga2 AI tool?
How does StableBeluga2 generate text?
What language does StableBeluga2 use for text generation?
What library is used in the implementation of the StableBeluga2?
How do I customize the output of StableBeluga2?
How does one incorporate StableBeluga2 into their code?
What is the training process for StableBeluga2?
What specific format does StableBeluga2 prompt need?
What kind of dataset was StableBeluga2 trained on?
What testing steps should be conducted before StableBeluga2 deployment?
What are some potential risks and limitations of StableBeluga2?
Are there any other versions of the StableBeluga model?
What is the purpose of the top-p and top-k parameters in StableBeluga2?
Who developed the StableBeluga2 AI tool and how can they be contacted?
What should I do if StableBeluga2 produces an objectionable response?
Is the StableBeluga2 licensed and how?
Can StableBeluga2 be used for conversation AI?
How do the 'User' and 'Assistant' components work in StableBeluga2?
Can StableBeluga2 be used in my application?
What does 'auto-regressive' mean with regards to StableBeluga2?

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