XINLI1997/ReMaRk-Qwen3.5-9B-RedditV2-SFT

VISIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:9BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 13, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

XINLI1997/ReMaRk-Qwen3.5-9B-RedditV2-SFT is a 9 billion parameter language model developed by Xin Li et al., based on the Qwen3.5-9B-Base architecture with a 32K context length. It is specifically fine-tuned using Supervised Fine-Tuning (SFT) on the Reddit-v2 (movies) dataset. This model is designed for conversational movie recommendation tasks, excelling at generating movie suggestions based on user preferences.

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Overview

XINLI1997/ReMaRk-Qwen3.5-9B-RedditV2-SFT is a 9 billion parameter language model, fine-tuned from the Qwen/Qwen3.5-9B-Base. Developed by Xin Li et al., this model is specialized for conversational movie recommendation, leveraging Supervised Fine-Tuning (SFT) on the Reddit-v2 (movies) dataset. It was released in conjunction with the paper "Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders" (arXiv: 2610.17771).

Key Capabilities

  • Specialized Movie Recommendation: Generates movie recommendations based on user input, formatted as "title (year)" lists.
  • Conversational Interface: Designed to respond to natural language queries for movie suggestions.
  • Qwen3.5 Architecture: Benefits from the underlying Qwen3.5-9B-Base model's capabilities, with a 32K context length.

Good For

  • Building Recommendation Systems: Ideal for applications requiring a dedicated movie recommender that can understand conversational prompts.
  • Research in LLM Recommenders: Serves as an SFT initialization checkpoint for further research, particularly in areas related to removing responder noise in recommendation systems.
  • Domain-Specific Applications: Suitable for use cases where movie recommendations are a primary function, rather than general-purpose assistance.

Limitations

  • Domain-Specific: Primarily 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 trained and optimized for English language interactions.

Finetunes
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