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
Align model card with released pipeline
Browse files
README.md
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# RARE-Task-Level
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This
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checkpoints from **RARE: Reinforcement Learning with Adaptive Rubric Evolution for Open-Ended Generation**.
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Within each production style, all queries share a six-criterion rubric that is
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periodically revised from cross-query on-policy evidence while preserving its
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IDs, order, score maxima, and reward aggregation.
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| Subfolder | Production style | Final RL step |
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respectively, for an equally weighted average of **73.89**. This is a 1.08-point
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gain over the matched Task-Static baseline.
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Each subfolder is an independent full
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`Qwen/Qwen3-4B-Instruct-2507`
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tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=variant)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id, subfolder=variant, device_map="auto", torch_dtype="auto"
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```
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Use the corresponding configuration from `EurekaTian/RARE` for the released
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system instruction and query format. The weights are licensed under Apache-2.0;
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the dataset is separately licensed under CC BY 4.0. The canonical paper citation
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will be added after publication metadata is finalized.
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# RARE-Task-Level
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This repository contains the final **task-level adaptive rubric**
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checkpoints from **RARE: Reinforcement Learning with Adaptive Rubric Evolution for Open-Ended Generation**.
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Within each production style, all queries share a six-criterion rubric that is
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periodically revised from cross-query on-policy evidence while preserving its
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IDs, order, score maxima, and reward aggregation.
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<p align="center">
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<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>
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<a href="https://openreview.net/forum?id=XgYh8jfypi"><img src="https://img.shields.io/badge/Paper-OpenReview-B31B1B?style=flat-square" alt="Paper" /></a>
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<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>
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<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>
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</p>
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| Subfolder | Production style | Final RL step |
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|---|---|---:|
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respectively, for an equally weighted average of **73.89**. This is a 1.08-point
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gain over the matched Task-Static baseline.
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Each subfolder is an independent inference-only full checkpoint derived from
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`Qwen/Qwen3-4B-Instruct-2507`; optimizer and trainer state are not included.
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The exact launcher and settings are documented in the
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[RARE repository](https://github.com/Eureka-Maggie/RARE#training). Use the
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corresponding configuration from [EurekaTian/RARE](https://huggingface.co/datasets/EurekaTian/RARE)
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for the released system instruction and query format. This card does not add a
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separate convenience inference command that was not part of the released
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pipeline. The weights are licensed under Apache-2.0;
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the dataset is separately licensed under CC BY 4.0. The canonical paper citation
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will be added after publication metadata is finalized.
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