XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-SFT

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:Transformer Featherless Exclusive Cold

XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-SFT is an 8 billion parameter language model, fine-tuned from Meta's Llama-3.1-8B-Instruct, specifically for conversational movie recommendations. Developed by Xin Li et al., this model uses Supervised Fine-Tuning (SFT) on the Reddit-v2 (movies) dataset, with a context length of 8192 tokens. Its primary differentiator is its specialization in generating movie recommendations based on user preferences, as detailed in the paper "Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders".

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Overview

XINLI1997/Llama-3.1-8B-ReMaRk-RedditV2-SFT is an 8 billion parameter language model, built upon Meta's Llama-3.1-8B-Instruct. It has been specifically fine-tuned using Supervised Fine-Tuning (SFT) on the Reddit-v2 (movies) dataset. This model is designed for conversational recommendation tasks within the movie domain, aiming to provide relevant suggestions based on user input. Its development is detailed in the research paper "Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders" by Xin Li et al. (arXiv: 2610.17771).

Key Capabilities

  • Conversational Movie Recommendation: Generates lists of movie recommendations in response to user preferences.
  • Specialized Fine-tuning: Utilizes SFT on a curated Reddit-v2 dataset focused on movie discussions and recommendations.
  • Llama 3.1 Base: Benefits from the underlying architecture and capabilities of the Llama 3.1-8B-Instruct model.

Good For

  • Building Recommendation Systems: Ideal for applications requiring a language model to provide movie suggestions.
  • Research in LLM Recommenders: Serves as an SFT initialisation checkpoint for further research, particularly in areas related to removing responder noise from GRPO for LLM recommenders.
  • Domain-Specific Chatbots: Suitable for creating chatbots focused solely on movie-related queries and recommendations.

Limitations

  • Domain-Specific: Trained exclusively for movie recommendations and not intended as a general-purpose assistant.
  • Potential Biases: 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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