shantanu61000/LTM-SFR-FINAL-R1
The shantanu61000/LTM-SFR-FINAL-R1 is an 8 billion parameter Qwen3-based causal language model developed by shantanu61000. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient finetuning process.
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Model Overview
The shantanu61000/LTM-SFR-FINAL-R1 is an 8 billion parameter language model based on the Qwen3 architecture. Developed by shantanu61000, this model was finetuned from unsloth/qwen3-8b-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen3-based, a powerful transformer architecture.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended outputs.
Intended Use Cases
This model is suitable for a variety of general natural language processing tasks, benefiting from its efficient finetuning and robust base architecture. Its 32K context window makes it particularly useful for applications requiring understanding or generation of longer texts.