bloomz-7b1-p3

Type: model

Publisher: BigScience Workshop Released: 2022-11-17 v.1.0.0

Model details

Blackbox External Model Access	✓
Capabilities demonstration	✓
Capabilities description	✓
Centralized model documentation	✓
Evaluation of capabilities	✓
External model access protocol	✓
External reproducibility of capabilities evaluation	✓
External reproducibility of intentional harm evaluation	-
External reproducibility of mitigations evaluation	-
External reproducibility of trustworthiness evaluation	-
External reproducibility of unintentional harm evaluation	-
Full external model access	✓
Inference compute evaluation	-
Inference duration evaluation	✓
Input modality	✓
Intentional harm evaluation	-

Limitations demonstration	-
Limitations description	✓
Mitigations demonstration	-
Mitigations description	-
Mitigations evaluation	-
Model architecture	✓
Asset license	✓
Model components	✓
Model size	✓
Output modality	✓
Risks demonstration	-
Risks description	-
Third party capabilities evaluation	-
Third party evaluation of limitations	✓
Third party mitigations evaluation	-
Third party risks evaluation	-
Trustworthiness evaluation	-
Unintentional harm evaluation	-

Natural language processing tasks, including but not limited to translation, sentiment analysis, and question answering.
Cross-lingual understanding and generation tasks.
Instruction-based prompt generation for a wide range of languages.
Zero-shot and few-shot learning applications.
Exploratory data analysis and research in multilingual language model capabilities.

No metrics specified.

Carbon emitted (tCO2eq): 0

Energy usage: 0 w

Compute usage: 0

Potential for biased or inaccurate outputs across less-supported languages, requiring careful validation.
Use of the model in applications with impactful consequences should be approached with caution.
Need for transparency regarding the training data sources and model limitations to users.
Ethical considerations around data privacy and consent, especially in multilingual contexts.
Awareness of cultural sensitivity and potential for reinforcing stereotypes must be considered in model application and development.

Employment of early stopping, addition of long tasks, and minimum generation length forcing for improved generative task performance.
Fine-tuning with both English and machine-translated multilingual prompts for enhanced cross-lingual abilities.
Utilization of the model in research to explore and expand the boundaries of zero-shot learning across languages.
Adoption of ethical and fair use practices, considering the model's broad linguistic capabilities.
Engagement with the BigScience community for collaborative research and development efforts.