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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and 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 end user interacts with comprehensively documented and 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 end user interacts with made freely available?
Training Code
Is the source code of dataset processing, model training and tuning comprehensively 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 available in standardized format that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation?
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?

SQLCoder

by Defog.ai

Text-to-SQL model.
Code
Latest
https://huggingface.co/defog/sqlcoder2
StarCoder2-15B
SQLCoder-2
CC-BY-SA-4.0 and Apache-2.0 and BigCode OpenRAIL-M v1 license agreement
Organization creating code generation for LLMs.
https://huggingface.co/defog
October 2023
Availability
Base Model Data
Unfiltered data sources described in paper.
https://arxiv.org/pdf/2402.19173
End User Model Data
Data sources published in GitHub repo
https://github.com/defog-ai/sql-eval
Base Model Weights
Weights made available on HuggingFace.
https://huggingface.co/bigcode/starcoder2-15b
End User Model Weights
Weights made available on HuggingFace.
https://huggingface.co/defog/sqlcoder2
Training Code
GitHub exists, but mainly contains inference code. Training procedure broadly outlined in text.
https://github.com/defog-ai/sqlcoderhttps://defog.ai/blog/open-sourcing-sqlcoder2-7bhttps://defog.ai/blog/sqlcoder2-technical-details
Documentation
Code Documentation
No code, so no documentation.
Hardware Architecture
No description of hardware architecture found.
Preprint
Info about model released through blog posts.
https://defog.ai/blog/open-sourcing-sqlcoder2-7bhttps://dl.acm.org/doi/pdf/10.1145/3531146.3533779
Paper
No peer-reviewed paper found.
Modelcard
Model card provides a decent amount of information.
https://huggingface.co/defog/sqlcoder2
Datasheet
Information regarding datasheet provided in detail.
https://huggingface.co/defog/sqlcoder2
Access
Package
No package found
API and Meta Prompts
Demo available
https://defog.ai/sqlcoder-demo
Licenses
Model almost fully open-source licensed, but inherits some properties from BigCode OpenRAIL-M v1 license agreement.
https://huggingface.co/defog/sqlcoder2
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