Text Generation
Transformers
Safetensors
Chinese
reinforcement-learning
grpo
adaptive-rubric
script-generation
Instructions to use EurekaTian/RARE-Task-Level with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EurekaTian/RARE-Task-Level with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EurekaTian/RARE-Task-Level")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EurekaTian/RARE-Task-Level", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EurekaTian/RARE-Task-Level with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EurekaTian/RARE-Task-Level" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EurekaTian/RARE-Task-Level", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EurekaTian/RARE-Task-Level
- SGLang
How to use EurekaTian/RARE-Task-Level with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "EurekaTian/RARE-Task-Level" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EurekaTian/RARE-Task-Level", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "EurekaTian/RARE-Task-Level" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EurekaTian/RARE-Task-Level", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EurekaTian/RARE-Task-Level with Docker Model Runner:
docker model run hf.co/EurekaTian/RARE-Task-Level
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Download README.md from EurekaTian/RARE-Task-Level: direct link, hf CLI and curl.
- Browser
- Download file 2.43 kB
-
https://huggingface.co/EurekaTian/RARE-Task-Level/resolve/main/README.md
- Command line
-
hf download hf://EurekaTian/RARE-Task-Level/README.md
-
curl -L -o README.md https://huggingface.co/EurekaTian/RARE-Task-Level/resolve/main/README.md
2.43 kB
| license: apache-2.0 | |
| language: | |
| - zh | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| base_model: Qwen/Qwen3-4B-Instruct-2507 | |
| datasets: | |
| - EurekaTian/RARE | |
| tags: | |
| - reinforcement-learning | |
| - grpo | |
| - adaptive-rubric | |
| - script-generation | |
| # RARE-Task-Level | |
| This repository contains the final **task-level adaptive rubric** | |
| checkpoints from **RARE: Reinforcement Learning with Adaptive Rubric Evolution for Open-Ended Generation**. | |
| Within each production style, all queries share a six-criterion rubric that is | |
| periodically revised from cross-query on-policy evidence while preserving its | |
| IDs, order, score maxima, and reward aggregation. | |
| <p align="center"> | |
| <a href="https://eureka-maggie.github.io/RARE/"><img src="https://img.shields.io/badge/Project-Page-52735C?style=flat-square" alt="Project page" /></a> | |
| <a href="https://openreview.net/forum?id=XgYh8jfypi"><img src="https://img.shields.io/badge/Paper-OpenReview-B31B1B?style=flat-square" alt="Paper" /></a> | |
| <a href="https://github.com/Eureka-Maggie/RARE"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=flat-square&logo=github" alt="Code" /></a> | |
| <a href="https://huggingface.co/datasets/EurekaTian/RARE"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-RARE-FFD21E?style=flat-square" alt="Dataset" /></a> | |
| </p> | |
| | Subfolder | Production style | Final RL step | | |
| |---|---|---:| | |
| | `2d_animation` | 2D animation | 132 | | |
| | `3d_stopmotion` | 3D and stop-motion animation | 108 | | |
| | `live_action` | Live action | 94 | | |
| The held-out scores are 76.27, 75.56, and 69.84 for the three styles, | |
| respectively, for an equally weighted average of **73.89**. This is a 1.08-point | |
| gain over the matched Task-Static baseline. | |
| Each subfolder is an independent inference-only full checkpoint derived from | |
| `Qwen/Qwen3-4B-Instruct-2507`; optimizer and trainer state are not included. | |
| The exact launcher and settings are documented in the | |
| [RARE repository](https://github.com/Eureka-Maggie/RARE#training). Use the | |
| corresponding configuration from [EurekaTian/RARE](https://huggingface.co/datasets/EurekaTian/RARE) | |
| for the released system instruction and query format. This card does not add a | |
| separate convenience inference command that was not part of the released | |
| pipeline. The weights are licensed under Apache-2.0; | |
| the dataset is separately licensed under CC BY 4.0. The canonical paper citation | |
| will be added after publication metadata is finalized. | |