Skywork-Reward-Llama-3.1-8B

An advanced reward model for text classification and preference assessment.

CommonProductProgrammingMachine LearningNatural Language Processing

Skywork-Reward-Llama-3.1-8B is an advanced reward model based on the Meta-Llama-3.1-8B-Instruct architecture. It has been trained using the Skywork Reward Data Collection, which consists of 80,000 high-quality preference pairs. The model excels in handling preferences in complex scenarios, particularly with challenging preference pairs spanning multiple domains, including mathematics, programming, and security. As of September 2024, the model ranks third on the RewardBench leaderboard.

Text classification: Capable of classifying text to determine its category.
Preference assessment: Handles complex preference pairs
providing scores for preference judgments.
High efficiency: Achieves high performance using a relatively small dataset and straightforward data organization techniques.
Multi-domain applications: Applicable across various fields such as mathematics
programming
and security.
High ranking: Demonstrates excellent performance on the RewardBench leaderboard.
Code examples: Provides sample code to help users understand and utilize the model effectively.
Community licensing: Supports community usage and commercial purposes in compliance with the Skywork community license agreement.

The target audience primarily includes data scientists
machine learning engineers
and researchers who require a high-performance model capable of handling complex preference judgments. Additionally
developers or businesses needing text classification and preference assessment capabilities may also benefit from this model.

Used for evaluating preferences in solving mathematical problems.
Used in programming to compare the quality of different code implementations.
Used in security to assess the safety of textual content.

Load the model and tokenizer: Use AutoModelForSequenceClassification and AutoTokenizer to load the pre-trained model.
Prepare conversation data: Format and tokenize the dialogues between the user and the assistant.
Obtain reward scores: Use the model to evaluate the formatted dialogues and retrieve reward scores.
Analyze results: Analyze and compare the quality of different dialogue content based on the reward scores.

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