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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?

StarCoder

by BigCode

Self-aligned LLM.
Code
Latest
https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1
StarCoder2-15B
StarCoder2-15B-Instruct-v0.1
BigCode OpenRAIL-M v1 license agreement
Open scientific collaboration for creating LLMs.
https://huggingface.co/bigcode
April 2024
Availability
Training Code
All training code made available on GitHub.
https://github.com/bigcode-project/selfcodealign
Base Model Data
Unfiltered data sources described in paper.
https://arxiv.org/pdf/2402.19173
End User Model Data
Unfiltered data sources described in paper.
https://arxiv.org/pdf/2410.24198
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/bigcode/starcoder2-15b-instruct-v0.1
Documentation
Code Documentation
Training code is well-documented.
https://github.com/bigcode-project/selfcodealign
Hardware Architecture
Hardware architecture described in paper.
https://arxiv.org/pdf/2410.24198
Preprint
Preprint made available on arXiv.
https://arxiv.org/pdf/2410.24198
Paper
Paper published in NeurIPS.
https://ccs.neu.edu/~arjunguha/main/papers/2024-wei-starcoder-self-alignment.html
Modelcard
Model card provides the requisite information.
https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1
Datasheet
No data sheet found.
Access
Package
Model available on Ollama.
https://ollama.com/library/starcoder2
API and Meta Prompts
No API found.
Licenses
BigCode OpenRAIL-M v1 license agreement, not an OSI-approved license.
https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1
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