Meta-rater Language Model (7.2B Parameters, 150B Tokens)

This repository contains the model described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.

Code: https://github.com/opendatalab/Meta-rater

Model Description

This is a 7.2B parameter transformer-based decoder-only language model trained from scratch on 150B tokens selected from SlimPajama dataset using the Meta-rater framework with all 25 quality scores. This represents the largest and most capable model in the Meta-rater research, demonstrating maximal benefits of quality-driven data selection at scale.

Model Details

Architecture: Transformer decoder-only
Parameters: 7.2B (7,241,732,096 parameters)
Training Tokens: 150B tokens
Context Window: 1,024 tokens
Vocabulary Size: 32,000 (LLaMA tokenizer)
Data Selection Method: Meta-rater with all 25 quality scores
Optimization: Learned optimal weightings from 1.3B experiments

Architecture Specifications

Hidden Dimension: 4,096
Number of Layers: 32
Attention Heads: 32
Key-Value Heads: 8 (Grouped Query Attention)
MLP Ratio: 8/3
Position Encoding: RoPE (base=10,000)

Data Selection Framework

The training data was selected using the complete Meta-rater framework, demonstrating its scalability:

Comprehensive Quality Assessment (25 metrics)

Natural Language Quality Signals (11): RedPajama rule-based measures
Data Importance Scores (3): DSIR similarity to Books, Wikipedia, AutoMathText
Model-based Ratings (11): PRRC + QuRating + FineWeb-Edu + WanjuanCC

Optimal Integration Strategy

The same learned weight distribution from 1.3B proxy experiments was applied, proving the transferability of the Meta-rater framework across scales.

Training Details

Hardware: 32x NVIDIA A800 GPUs
Global Batch Size: 4,194,304 tokens
Learning Rate: 5e-5
Optimizer: Adam (β₁=0.9, β₂=0.95, ε=1e-8)
Training Time: ~284 hours

Performance Results

Downstream Task Performance (Average Accuracy)

General Knowledge: 67.97% (+2.87% vs Random 7.2B)
- ARC-Easy: 71.34%
- ARC-Challenge: 39.76%
- SciQ: 92.80%
Commonsense Reasoning: 54.58% (+2.57% vs Random 7.2B)
- HellaSwag: 58.97%
- SIQA: 44.32%
- WinoGrande: 60.45%
Reading Comprehension: 37.14% (+1.27% vs Random 7.2B)
- RACE: 36.08%
- OpenbookQA: 38.20%
Overall Average: 55.24% (+3.12% vs Random 7.2B)

Knowledge-Intensive Tasks

MMLU: 26.24% (+0.03% vs Random 7.2B)
NaturalQuestions: 10.42% (-0.47% vs Random 7.2B)

Scaling Excellence

Meta-rater Scaling Progression

1.3B Meta-rater: 47.01% overall
3.3B Meta-rater: 54.71% overall (+7.70%)
7.2B Meta-rater: 55.24% overall (+0.53%)

Scaling Efficiency Comparison

Meta-rater vs Random across scales:

1.3B: +3.23% improvement (Meta-rater advantage)
3.3B: +1.73% improvement (maintained advantage)
7.2B: +3.12% improvement (increased advantage)

Key Research Findings

Data Quality Becomes More Valuable at Scale

Larger Improvement: 7.2B shows biggest absolute improvement (+3.12%)
Efficiency Recovery: Meta-rater overcomes random selection plateau
Continued Benefits: Quality selection prevents performance stagnation
Scale Synergy: Larger models better utilize high-quality data

Breakthrough Performance

This model demonstrates:

Highest Absolute Performance: 55.24% overall accuracy
Best Scaling Efficiency: Largest improvement over random baseline
Consistent Quality: Strong performance across all task categories
Framework Validation: Meta-rater scales effectively to 7.2B parameters

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "opendatalab/meta-rater-7b-25raters"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text (highest quality model)
prompt = "The implications of quantum computing for cryptography"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_length=200,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Applications

This model is exceptionally well-suited for:

Production deployment requiring highest quality standards
Research applications needing state-of-the-art baselines
Content generation at scale with quality assurance
Educational platforms across diverse domains
Knowledge-intensive applications and question answering
Professional writing assistance and content creation
Multi-domain tasks requiring robust capabilities

Revolutionary Advantages

Quality Data Selection at Scale

Overcomes Plateau: While random selection stagnates, Meta-rater continues improving
Efficiency Multiplication: 3.12% improvement represents significant capability gain
Resource Optimization: Same training cost, substantially better results
Scalability Proof: Validates data curation importance at any scale

State-of-the-Art Achievement

Best Performance: Highest accuracy among all tested models and methods
Largest Improvement: Biggest relative gain over random baseline
Comprehensive Excellence: Strong across all evaluation categories
Framework Validation: Proves Meta-rater methodology at scale

Research Significance

This model provides definitive evidence that:

Data quality matters more at scale: Larger models need better data
Meta-rater scaling: Framework benefits increase rather than diminish
Efficiency paradigm: Quality beats quantity in data selection
Practical impact: Substantial performance gains with existing computational budgets

Strengths

Highest Performance: Best accuracy across all model scales
Scaling Success: Demonstrates continued Meta-rater benefits at scale
Quality Consistency: Reliable high-quality output generation
Resource Efficiency: Maximum return on computational investment
Robust Capabilities: Strong performance across diverse tasks
Research Validated: Empirically proven methodology

Limitations

Computational Requirements: Large model requires significant resources
Context Window: Limited to 1,024 tokens
No Instruction Tuning: Base model without safety alignment
Data Selection Overhead: Requires quality score preprocessing
Specialized Infrastructure: Needs appropriate hardware for deployment

Critical Insights

Data Curation Imperative

This model definitively proves:

Scale Amplifies Quality: Better data becomes more important with larger models
Random Selection Failure: Quality-agnostic approaches hit performance walls
Meta-rater Success: Systematic quality integration scales effectively
Future Direction: Data curation essential for continued progress

Performance Breakthrough

vs Random 7.2B: +3.12% improvement (largest in study)
vs Best Single Method: Outperforms all baseline approaches
vs Simple Combinations: Superior to naive quality score averaging
vs Previous SOTA: Establishes new state-of-the-art for data selection

Citation

If you use this model in your research, please cite:

@article{zhuang2025meta,
  title={Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models},
  author={Zhuang, Xinlin and Peng, Jiahui and Ma, Ren and Wang, Yinfan and Bai, Tianyi and Wei, Xingjian and Qiu, Jiantao and Zhang, Chi and Qian, Ying and He, Conghui},
  journal={arXiv preprint arXiv:2504.14194},
  year={2025}
}

Related Resources

Complete Model Series: 1.3B, 3.3B, and 7.2B variants
PRRC Rating Models: Quality assessment models for data curation
Annotated SlimPajama: Fully labeled dataset with all quality scores
Meta-rater Framework: Implementation and methodology details

License

Please refer to the license terms of the original SlimPajama dataset and follow applicable data licensing requirements.

Contact

For questions or issues, please contact the authors or open an issue in the repository.

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meta-rater-7b-25raters