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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

by 01.AI

The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI. Targeted as a bilingual language model and trained on 3T multilingual corpus.
Text
Full
https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K
Yi-34B
Yi-1.5-34B-Chat-16K
Apache 2.0
Chinese AI start-up.
https://www.01.ai/
May 2024
Availability
Training Code
repository contains some code for demos and for instruction tuning, but only sparse examples of code for initial training and model architecture
https://github.com/01-ai/Yi
Base Model Data
Training data for base model undocumented, though preprint mentions CommonCrawl as one source
End User Model Data
No end model data sources found.
Base Model Weights
Base model weights shared via HuggingFace
https://huggingface.co/01-ai/Yi-34B
End User Model Weights
Instruction-tuned weights shared via HuggingFace.
https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K
Documentation
Code Documentation
Inference and fine-tuning code is documented, training code is not documented.
https://github.com/01-ai/Yi/
Hardware Architecture
Model architecture described in paper.
https://arxiv.org/pdf/2403.04652
Preprint
Preprint provides some info on pretraining data (CommonCrawl) but none on instruction tuning dataset.
https://arxiv.org/abs/2403.04652
Paper
No paper found
Modelcard
Model card provides limited relevant information.
https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K
Datasheet
No data sheets found.
Access
Package
Model available on Ollama.
https://ollama.com/library/yi
API and Meta Prompts
Model is gated and commercially available.
https://platform.01.ai/docs
Licenses
Model licensed under Apache 2.0
https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K
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