Instructions to use opendatalab/meta-rater-3b-25raters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opendatalab/meta-rater-3b-25raters with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="opendatalab/meta-rater-3b-25raters", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("opendatalab/meta-rater-3b-25raters", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use opendatalab/meta-rater-3b-25raters with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opendatalab/meta-rater-3b-25raters"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opendatalab/meta-rater-3b-25raters",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/opendatalab/meta-rater-3b-25raters

SGLang

How to use opendatalab/meta-rater-3b-25raters 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 "opendatalab/meta-rater-3b-25raters" \
    --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": "opendatalab/meta-rater-3b-25raters",
		"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 "opendatalab/meta-rater-3b-25raters" \
        --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": "opendatalab/meta-rater-3b-25raters",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use opendatalab/meta-rater-3b-25raters with Docker Model Runner:
```
docker model run hf.co/opendatalab/meta-rater-3b-25raters
```

renma commited on Jun 11, 2025

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7fcf474

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@@ -10,6 +10,10 @@ pipeline_tag: text-generation
 # Meta-rater Language Model (3.3B Parameters, 100B Tokens)
 ## Model Description
 This is a 3.3B parameter transformer-based decoder-only language model trained from scratch on 100B tokens selected from SlimPajama dataset using the **Meta-rater** framework with all 25 quality scores. This model demonstrates the scalability of Meta-rater's data selection benefits to larger model sizes and training datasets.

 # Meta-rater Language Model (3.3B Parameters, 100B Tokens)
+This repository contains the model described in the paper [Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models](https://huggingface.co/papers/2504.14194).
+Code: https://github.com/opendatalab/Meta-rater
 ## Model Description
 This is a 3.3B parameter transformer-based decoder-only language model trained from scratch on 100B tokens selected from SlimPajama dataset using the **Meta-rater** framework with all 25 quality scores. This model demonstrates the scalability of Meta-rater's data selection benefits to larger model sizes and training datasets.