Hakid/qwen25-3b-alpaca-id-qlora

TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 1, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Hakid/qwen25-3b-alpaca-id-qlora is a 3.1 billion parameter Qwen2.5-based causal language model developed by Hakid. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and performance, making it suitable for applications requiring a compact yet capable LLM.

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Model Overview

Hakid/qwen25-3b-alpaca-id-qlora is a 3.1 billion parameter language model based on the Qwen2.5 architecture. Developed by Hakid, this model has been fine-tuned using the Unsloth library and Huggingface's TRL, which significantly accelerated its training process.

Key Characteristics

  • Base Model: Qwen2.5-3B-Instruct
  • Parameter Count: 3.1 billion parameters
  • Training Efficiency: Utilizes Unsloth for 2x faster fine-tuning, making it an efficient choice for developers.
  • Context Length: Supports a context length of 32768 tokens, allowing for processing of substantial input.

Use Cases

This model is particularly well-suited for applications where computational resources are a consideration, but a capable language model is still required. Its efficient training and compact size make it ideal for:

  • Edge device deployment
  • Applications requiring faster iteration cycles
  • General natural language understanding and generation tasks where a 3B parameter model is sufficient.

Finetunes
1 models