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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?

OpenCodeInterpreter

by Multimodal Art Projection

Coder model integrating execution and iterative refinement functionalities.
Code
Full
https://huggingface.co/m-a-p/OpenCodeInterpreter-CL-70B
Llama-2-70B
OpenCodeInterpreter-CL-70B
Apache-2.0
Open-source AI research community.
https://m-a-p.ai/
February 2024
Availability
Training Code
Repo exists, but does not contain training code.
https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/
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
Dataset for end model published on HuggingFace. Proprietary dataset used in intermediate CodeLlama model.
https://huggingface.co/datasets/m-a-p/Code-Feedback
Base Model Weights
Gated model available on HuggingFace.
https://huggingface.co/meta-llama/Llama-2-70b-hf
End User Model Weights
Model weights published on HuggingFace.
https://huggingface.co/datasets/m-a-p/Code-Feedback
Documentation
Code Documentation
No code, so no documentation.
Hardware Architecture
No hardware architecture description provided.
Preprint
Preprint made available through arXiv.
https://arxiv.org/pdf/2402.14658
Paper
Paper published in ACL.
https://aclanthology.org/2024.findings-acl.762/
Modelcard
Model card provides minimal information, only describing benchmarking and usage.s
https://huggingface.co/m-a-p/OpenCodeInterpreter-CL-70B
Datasheet
Datasheet for base model unavailable. Datasheet for end model contains limited information.
https://huggingface.co/datasets/m-a-p/Code-Feedback
Access
Package
No package found.
API and Meta Prompts
HuggingFace space containing model available.
https://huggingface.co/spaces/m-a-p/OpenCodeInterpreter_demo
Licenses
Apache-2.0, an OSI-approved license.
https://huggingface.co/m-a-p/OpenCodeInterpreter-CL-70B
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