Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

@jonathanxu81205 - add llava-critic-1 #234

Merged
merged 2 commits into from
Jan 17, 2025
Merged

@jonathanxu81205 - add llava-critic-1 #234

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9ab4a6d
add assets identified by bot
Oct 6, 2024
32b4fdb
Update assets/bytedance.yaml
jxue16 Jan 17, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 22 additions & 0 deletions assets/bytedance.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -49,3 +49,25 @@
prohibited_uses: unknown
monitoring: unknown
feedback: https://huggingface.co/ByteDance/SDXL-Lightning/discussions
- type: model
name: LLaVA-Critic
organization: ByteDance and University of Maryland, College Park
description: LLaVA-Critic is an open-source large multimodal model (LMM) designed as a generalist evaluator. It assesses performance across a variety of multimodal tasks by following a high-quality critic instruction dataset, incorporating diverse evaluation criteria. The model is effective in areas like LMM-as-a-Judge, providing reliable evaluation scores comparable to GPT models, and Preference Learning, offering reward signals for preference learning to enhance model alignment capabilities.
created_date: 2024-10-06
url: https://arxiv.org/pdf/2410.02712
model_card: unknown
modality: image, text; text
analysis: LLaVA-Critic was tested in scenarios such as LMM-as-a-Judge and Preference Learning, showing a high correlation with commercial GPT models in evaluation scores. It served as an alternative to expensive human feedback in resource-constrained settings and demonstrated better performance in providing AI-generated feedback for model alignment compared to human-reliant reward models.
size: unknown
dependencies: []
training_emissions: unknown
training_time: unknown
training_hardware: unknown
quality_control: The model ensures quality by utilizing a high-quality dataset for critic instructions, providing both quantitative judgments and reasoning, with transparency in assessments.
access: open
license: Apache 2.0
intended_uses: The model can be used for evaluating multimodal tasks, generating reward signals for preference learning, and serving as a reliable alternate judge for model assessments.
prohibited_uses: The model should not be used in scenarios requiring authorization from proprietary models, nor relied upon for critical applications without human oversight due to potential biases in dataset.
monitoring: unknown
feedback: unknown

Loading