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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?

WizardLM

by Microsoft & Peking University

Empowering Large Pre-Trained Language Models to Follow Complex Instructions
Text
Full
https://github.com/nlpxucan/WizardLM
LLaMA-7B
Evol-Instruct (synthetic)
Llama 2 license
Microsoft, a major tech company, and Peking University, a Chinese university.
https://github.com/nlpxucan
April 2023
Availability
Training Code
Reasonably useful source code repository but as this is based on Llama, the underlying source is not available.
https://github.com/nlpxucan/WizardLM/tree/main/WizardLM
Base Model Data
Based on LLaMA, which is claimed to be public but nowhere exactly documented.
https://github.com/opening-up-chatgpt/opening-up-chatgpt.github.io/blob/main/projects/llama-2-chat.yaml
End User Model Data
The Evol-Instruct dataset contains 70k instruction-following sequences generated from Evol-Instruct
https://github.com/nlpxucan/WizardLM/tree/main/WizardLM#training-data
Base Model Weights
Based on LLaMA weights, which are not openly available though a leaked versions is in wide circulation.
End User Model Weights
Model weights offered as a delta to LLaMA
https://huggingface.co/WizardLM/WizardLM-7B-V1.0/tree/main
Documentation
Code Documentation
Code is comprehensively documented and contains demos.
https://github.com/nlpxucan/WizardLM/tree/main/WizardLM
Hardware Architecture
Architecture described in preprint and partly accessible in code repository
https://arxiv.org/abs/2304.12244
Preprint
Preprint describes method for creating large amounts of LLM-based synthetic RLHF data and fine-tuning WizardLM based on it
https://arxiv.org/abs/2304.12244
Paper
No peer-reviewed paper or data audit found. Preprint mentions it is 'under review'.
Modelcard
Model card is available, however contains links to pages talking about the model architecture, training, fine-tuning, and evaluation rather than containing them itself.
https://huggingface.co/WizardLM/WizardLM-7B-V1.0
Datasheet
Dataset card is available, but provides no information about data collection and curation. The preprint outlines data collection (based on a 52K instruction dataset from Alpaca) and curation at a high level.
https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k
Access
Package
Model available on Ollama.
https://ollama.com/library/wizardlm2
API and Meta Prompts
No API available
Licenses
Restricted for academic research purposes only. Code and Model diff release under CC-BY-NC-4.0, software code under Apache 2.0
https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/MODEL_DIFF_LICENSE
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