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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?

Yi-Coder

by 01.AI

Arguable that Yi-6B is the base model instead, since Yi-9B was created from Yi-6B. Unclear whether the code model was based on the regular 9B model or the context-length-extended one.
Code
Latest
https://huggingface.co/01-ai/Yi-Coder-9B-Chat
Yi-9B
Yi-Coder-9B-Chat
Apache 2.0
Chinese AI start-up.
https://www.01.ai/
July 2024
Availability
Training Code
GitHub repo does not appear to contain training code.
https://github.com/01-ai/Yi
Base Model Data
Pretraining data only described as coming from 'various sources'.
https://arxiv.org/pdf/2403.04652
End User Model Data
No information found about data sources of the end model.
Base Model Weights
Weights made available through HuggingFace.
https://huggingface.co/01-ai/Yi-9B
End User Model Weights
Weights made available through HuggingFace.
https://huggingface.co/01-ai/Yi-Coder-9B-Chat
Documentation
Code Documentation
No training code, so no documentation.
https://github.com/01-ai/Yi
Hardware Architecture
Hardware architecture seemingly only described for Yi-VL series of models.
https://arxiv.org/pdf/2403.04652
Preprint
Preprint made available on arXiv.
https://arxiv.org/pdf/2403.04652
Paper
No paper found.
Modelcard
Model card only provides a high-level introduction and usage instructions.
https://huggingface.co/01-ai/Yi-Coder-9B-Chat
Datasheet
No datasheet found.
Access
Package
Model available on Ollama.
https://ollama.com/library/yi-coder
API and Meta Prompts
Commercial API available.
https://platform.01.ai/docs
Licenses
Apache-2.0, an OSI-approved license.
https://huggingface.co/01-ai/Yi-Coder-9B-Chat
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