bqbbao6/Qwen2.5-1.5B-LoREonDGNL

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 10, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The bqbbao6/Qwen2.5-1.5B-LoREonDGNL is a 1.5 billion parameter Qwen2.5-based causal language model, finetuned by bqbbao6 from unsloth/Qwen2.5-1.5B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It features a 32768 token context length and is optimized for tasks benefiting from efficient finetuning techniques.

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Model Overview

The bqbbao6/Qwen2.5-1.5B-LoREonDGNL is a 1.5 billion parameter language model, finetuned by bqbbao6. It is based on the Qwen2.5 architecture and was specifically adapted from the unsloth/Qwen2.5-1.5B-Instruct model.

Key Characteristics

  • Architecture: Qwen2.5-based, a causal language model.
  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens.
  • Training Efficiency: This model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.

Use Cases

This model is particularly well-suited for applications where efficient finetuning on specific datasets is crucial. Its optimized training process makes it a strong candidate for:

  • Rapid adaptation to new domains or tasks.
  • Developing specialized instruction-following agents.
  • Scenarios requiring a capable language model with a moderate parameter count and extended context.

Finetunes
1 models