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Parameter descriptions:

Base Model Data
Are datasources for training the base model comprehensively documented and 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 end user interacts with comprehensively documented and 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 end user interacts with made freely available?
Training Code
Is the source code of dataset processing, model training and tuning comprehensively 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 available in standardized format that provides comprehensive insight on model architecture, training, fine-tuning, and evaluation?
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?

Vicuna

by LMSYS

Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
Text
Full
https://huggingface.co/lmsys/vicuna-13b-v1.3
Vicuna-13B
Vicuna-13B-v1.3
Non-commercial license
According to its website, 'The Large Model Systems Organisation develops large models and systems that are open, accessible and scalable'
https://lmsys.org/
March 2023
Availability
Base Model Data
Vicuna is fine-tuned LLaMA, and LLaMA in turn is based on 'publicly available datasets' that are not all specified or easily downloadable.
https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#training-dataset
End User Model Data
From the documentation 'We will not release the ShareGPT dataset'. Also 'Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com.'
https://github.com/lm-sys/FastChat#fine-tuning
Base Model Weights
Unlike Vicuna 13B v0, these weights do not require applying delta
https://github.com/lm-sys/FastChat#vicuna-weights
End User Model Weights
No model weights are shared for the instruction tuning
https://github.com/lm-sys/FastChat#fine-tuning
Training Code
Actively maintained repository
https://github.com/lm-sys/FastChat
Documentation
Code Documentation
Code is quite well-documented and released as part of the FastChat framework.
https://github.com/lm-sys/FastChat
Hardware Architecture
Preprint
Preprint covers training of the Vicuna model.
https://arxiv.org/pdf/2306.05685.pdf
Paper
No peer-reviewed paper.
Modelcard
Minimal model card, but many details are not provided or have to be pieced together from elsewhere.
https://huggingface.co/lmsys/vicuna-13b-v1.3
Datasheet
No datasheet provided.
Access
Package
Model available on Ollama.
https://ollama.com/library/vicuna
API and Meta Prompts
Support provided for several APIs OpenAI restful, HuggingFace, Langchain
https://github.com/lm-sys/FastChat#api
Licenses
From the documentation 'Vicuna is based on LLaMA and should be used under LLaMA's model license.'
https://github.com/lm-sys/FastChat#vicuna-weights
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