shantanu61000/LTM-SFR-9B

VISIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Jun 18, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

shantanu61000/LTM-SFR-9B is a 9 billion parameter causal language model, finetuned from Qwen/Qwen3.5-9B. Developed by shantanu61000, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. It is designed for general language tasks, leveraging its Qwen3.5 base and efficient finetuning process.

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Model Overview

shantanu61000/LTM-SFR-9B is a 9 billion parameter language model, finetuned by shantanu61000 from the Qwen/Qwen3.5-9B base model. This model was developed with a focus on training efficiency, utilizing the Unsloth library in conjunction with Huggingface's TRL library. This combination enabled a reported 2x faster finetuning process compared to standard methods.

Key Characteristics

  • Base Model: Finetuned from Qwen/Qwen3.5-9B, inheriting its foundational capabilities.
  • Parameter Count: 9 billion parameters, offering a balance between performance and computational requirements.
  • Training Efficiency: Leverages Unsloth for accelerated finetuning, indicating potential for rapid adaptation to specific tasks.
  • License: Distributed under the Apache-2.0 license, allowing for broad use and modification.

Potential Use Cases

Given its Qwen3.5 base and efficient finetuning, this model is suitable for a variety of general-purpose natural language processing tasks, including:

  • Text generation and completion.
  • Summarization.
  • Question answering.
  • Chatbot development.

Its optimized training process suggests it could be a good candidate for developers looking to quickly deploy or further adapt a capable 9B model.

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