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omnireward-factory

https://github.com/jinzhuoran/omnireward-factory

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (4.0%) to scientific vocabulary
Last synced: 8 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: jinzhuoran
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 9.25 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 11 months ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

OmniReward-Factory

🚀 Installation

bash git clone https://github.com/jinzhuoran/OmniReward-Factory.git conda create -n omnireward python=3.10 conda activate omnireward

We recommend using torch==2.2.0 for best compatibility.

Install PyTorch (choose one based on your CUDA version):

```bash

For CUDA 11.8:

pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 \ --index-url https://download.pytorch.org/whl/cu118

For CUDA 12.1:

pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 \ --index-url https://download.pytorch.org/whl/cu121 ```

Then install the remaining dependencies:

bash cd OmniReward-Factory pip install -r requirements.txt

🏋️‍♀️ Training Omni-Reward

bash bash scripts/train.sh bash scripts/train_t2t.sh bash scripts/train_ti2t.sh bash scripts/train_t2iv.sh

Owner

  • Name: Zhuoran Jin
  • Login: jinzhuoran
  • Kind: user
  • Location: Beijing
  • Company: NEU & CASIA

NLPer

Citation (CITATION.cff) 

cff-version: 1.2.0
date-released: 2024-03
message: "If you use this software, please cite it as below."
authors:
- family-names: "Zheng"
  given-names: "Yaowei"
- family-names: "Zhang"
  given-names: "Richong"
- family-names: "Zhang"
  given-names: "Junhao"
- family-names: "Ye"
  given-names: "Yanhan"
- family-names: "Luo"
  given-names: "Zheyan"
- family-names: "Feng"
  given-names: "Zhangchi"
- family-names: "Ma"
  given-names: "Yongqiang"
title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
url: "https://arxiv.org/abs/2403.13372"
preferred-citation:
  type: conference-paper
  conference:
    name: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
  authors:
    - family-names: "Zheng"
      given-names: "Yaowei"
    - family-names: "Zhang"
      given-names: "Richong"
    - family-names: "Zhang"
      given-names: "Junhao"
    - family-names: "Ye"
      given-names: "Yanhan"
    - family-names: "Luo"
      given-names: "Zheyan"
    - family-names: "Feng"
      given-names: "Zhangchi"
    - family-names: "Ma"
      given-names: "Yongqiang"
  title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
  url: "https://arxiv.org/abs/2403.13372"
  year: 2024
  publisher: "Association for Computational Linguistics"
  address: "Bangkok, Thailand"

GitHub Events

Total
  • Push event: 3
  • Create event: 2
Last Year
  • Push event: 3
  • Create event: 2

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 5
  • Total Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 5
  • Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
jinzhuoran z****n@n****n 5
Committer Domains (Top 20 + Academic)
nlpr.ia.ac.cn: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
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Dependencies

docker/docker-cuda/Dockerfile docker
  • ${BASE_IMAGE} latest build
docker/docker-cuda/docker-compose.yml docker
docker/docker-npu/Dockerfile docker
  • ascendai/cann 8.0.rc1-910b-ubuntu22.04-py3.8 build
docker/docker-npu/docker-compose.yml docker
docker/docker-rocm/Dockerfile docker
  • hardandheavy/transformers-rocm 2.2.0 build
docker/docker-rocm/docker-compose.yml docker
pyproject.toml pypi
requirements.txt pypi
  • accelerate >=0.34.0,<=1.2.1
  • av *
  • datasets >=2.16.0,<=3.2.0
  • einops *
  • fastapi *
  • fire *
  • gradio >=4.38.0,<=5.12.0
  • matplotlib >=3.7.0
  • numpy <2.0.0
  • packaging *
  • pandas >=2.0.0
  • peft >=0.11.1,<=0.12.0
  • protobuf *
  • pydantic *
  • pyyaml *
  • scipy *
  • sentencepiece *
  • sse-starlette *
  • tiktoken *
  • tokenizers >=0.19.0,<=0.21.0
  • transformers >=4.41.2,<=4.48.2,
  • transformers >=4.41.2,<=4.45.2
  • trl >=0.8.6,<=0.9.6
  • tyro <0.9.0
  • uvicorn *
setup.py pypi