Tool Information
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.
F.A.Q (20)
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.
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.
StableBeluga2 uses English for text generation.
The HuggingFace Transformers library is used in the implementation of the StableBeluga2 model.
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.
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.
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.
The prompt for StableBeluga2 follows a specific format that includes a system prompt, a user prompt, and an assistant output.
StableBeluga2 was trained on an internal Orca-style dataset.
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.
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.
Yes, there are other versions of the StableBeluga model. These include StableBeluga 1 - Delta, StableBeluga 13B, and StableBeluga 7B.
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.
StableBeluga2 was developed by Stability AI. For any queries or comments about the model, you can contact them via email at [email protected].
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.
Yes, StableBeluga2 is licensed. It's licensed under the STABLE BELUGA NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT.
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.
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.
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.
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.
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
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