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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and freely made available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Data
Are datasources for training the model that the enduser interacts with comprehensively documented and freely made available?
Base Model Weights
Are the weights of the base models made freely available? In case a distinction between base (foundation) and end (user) model is not applicable, this mirrors the end model data entries.
End User Model Weights
Are the weights of the model that the enduser interacts with made freely available?
Training Code
Is the source code of datasource processing, model training and tuining comprehensively and freely made available?
Code Documentation
Is the source code of datasource processing, model training and tuning comprehensively documented?
Hardware Architecture
Is the hardware architecture used for datasource processing and model training comprehensively documented?
Preprint
Are archived preprint(s) are available that detail all major parts of the system including datasource processing, model training and tuning steps?
Paper
Are peer-reviewed scientific publications available that detail all major parts of the system including datasource processing, model training and tuning steps?
Modelcard
Is a model card in standardized format available that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation are available?
Datasheet
Is a datasheet as defined in "Datasheets for Datasets" (Gebru et al. 2021) available?
Package
Is a packaged release of the model available on a software repository (e.g. a Python Package Index, Homebrew)?
API and Meta Prompts
Is an API available that provides unrestricted access to the model (other than security and CDN restrictions)? If applicable, this entry also collects information on the use and availability of meta prompts.
Licenses
Is the project fully covered by Open Source Initiative (OSI)-approved licenses, including all data sources and training pipeline code?

MPT

by Databricks

Open LLM by Databricks.
Text
Full
https://huggingface.co/mosaicml/mpt-30b-instruct
MPT-30B
MPT-30B-Instruct
Apache 2.0
Databricks, a data platform.
https://www.databricks.com
June 2023
Availability
Training Code
Codebase part of LLM foundry
https://github.com/mosaicml/llm-foundry/tree/main/llmfoundry/models/mpt
Base Model Data
C4 is part of the dataset but a precise specification of source data is hard to find
https://huggingface.co/datasets/c4
End User Model Data
dolly-hhrlhf, combination of Databrick dolly-15k dataset and a filtered subset of Anthropic HH-RLHF
https://huggingface.co/datasets/mosaicml/dolly_hhrlhf
Base Model Weights
Weights available via HuggingFace
https://huggingface.co/mosaicml/mpt-30b-instruct/tree/main
End User Model Weights
Documentation
Code Documentation
LLM Foundry codebase is well-documented and in active development.
https://github.com/mosaicml/llm-foundry/
Hardware Architecture
Architecture reasonably well-documented
https://huggingface.co/mosaicml/mpt-30b-instruct
Preprint
Paper
Modelcard
Modelcard is somewhat lacking in detail
https://huggingface.co/mosaicml/mpt-30b-instruct
Datasheet
Datasheet not available; data somewhat documented in blog post at link
https://www.mosaicml.com/blog/mpt-30b
Access
Package
API and Meta Prompts
API via HuggingFace
Licenses
Apache 2.0
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