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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?

InternLM

by Shanghai AI Laboratory

Leading open-source instruction-following model.
Text
Full
https://huggingface.co/internlm/internlm3-8b-instruct
InternLM3-8B
InternLM3-8B-Instruct
Apache-2.0
National-level Chinese research institute.
https://www.shlab.org.cn/
March 2024
Availability
Training Code
some shared
https://github.com/InternLM/InternLM?tab=readme-ov-file
Base Model Data
mainly from common crawl otherwise no details in preprint
https://arxiv.org/pdf/2403.17297
End User Model Data
not found
Base Model Weights
https://huggingface.co/internlm/internlm3-8b-instruct
End User Model Weights
https://huggingface.co/internlm/internlm3-8b-instruct
Documentation
Code Documentation
some code documented
https://github.com/InternLM/InternLM?tab=readme-ov-file
Hardware Architecture
Preprint
https://arxiv.org/abs/2403.17297
Paper
Modelcard
https://huggingface.co/internlm/internlm3-8b-instruct
Datasheet
Access
Package
Older version of model available on Ollama.
https://ollama.com/library/internlm2
API and Meta Prompts
Licenses
weights under apache 2
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