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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?

CodeLlama

by Meta

Coder model by Meta.
Code
Full
https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf
Llama-2-70B
CodeLlama-70B-Instruct
Llama 2 License
Meta, a major technology company.
https://ai.meta.com/
March 2024
Availability
Training Code
Repo exists, but does not contain training code.
https://github.com/meta-llama/codellama
Base Model Data
Data nowhere disclosed or documented, and described only in the vaguest terms in a corporate preprint released by Meta
End User Model Data
Proprietary dataset used.
Base Model Weights
Gated model available on HuggingFace.
https://huggingface.co/meta-llama/Llama-2-70b-hf
End User Model Weights
Gated model available on HuggingFace.
https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf
Documentation
Code Documentation
No training code, so undocumented.
Hardware Architecture
Hardware architecture discussed in aggregate and with a low level of detail in the model's paper.
https://arxiv.org/pdf/2308.12950
Preprint
Preprint available through arXiv.
https://arxiv.org/pdf/2308.12950
Paper
No peer-reviewed paper found.
Modelcard
Model card provides some information about training and inference, however mostly contains usage instructions.
https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf
Datasheet
No datasheet found.
Access
Package
Model available on Ollama.
https://ollama.com/library/codellama
API and Meta Prompts
Model available on various APIs.
Licenses
Llama 2 Community License Agreement, not an OSI-approved license.
https://huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf#model-details
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