XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-RankGRPO-s3407

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Oct 13, 2026License:llama3.1Architecture:Transformer0.0K Featherless Exclusive Cold

XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-RankGRPO-s3407 is an 8 billion parameter language model based on Llama 3.1, fine-tuned by Xin Li and collaborators using the Rank-GRPO objective on the Reddit-v2 (movies) dataset. This model specializes in conversational movie recommendations, generating lists of titles based on user preferences. It is designed to provide personalized recommendations by leveraging community-based data, making it suitable for applications requiring domain-specific recommendation systems.

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Overview

XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-RankGRPO-s3407 is an 8 billion parameter language model built upon the Llama 3.1-8B-Instruct base model. Developed by Xin Li and collaborators, this model is a Rank-GRPO baseline fine-tuned on the Reddit-v2 (movies) dataset, specifically for conversational recommendation tasks. It was released in conjunction with the paper Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders.

Key Capabilities

  • Conversational Movie Recommendations: Generates lists of movie titles based on user input, mimicking a conversational recommender.
  • Specialized Fine-tuning: Utilizes the Rank-GRPO objective, a reinforcement learning approach, to optimize recommendation quality.
  • Reddit-v2 Dataset: Trained on a processed version of the Reddit-v2 dataset, focusing on movie-related discussions and recommendations.

Good For

  • Domain-Specific Recommendation Systems: Ideal for applications requiring highly specialized recommendation capabilities within the movie domain.
  • Research in LLM Recommenders: Serves as a valuable baseline for researchers exploring reinforcement learning objectives like Rank-GRPO in large language models.
  • Interactive Content Discovery: Can be integrated into platforms where users seek personalized movie suggestions through natural language interaction.

Limitations

  • Domain Specificity: Exclusively trained for movie recommendations and not intended as a general-purpose assistant.
  • Bias Reflection: Recommendations may reflect biases present in the Reddit community data and favor popular items.
  • English Only: The model is designed for English language interactions.

Finetunes
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