ertghiu256/Qwen3-1.7B-tiny-orchestrator
The ertghiu256/Qwen3-1.7B-tiny-orchestrator is a 2 billion parameter Qwen3 model developed by ertghiu256, finetuned from unsloth/qwen3-1.7b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. With a 32K context length, it is optimized for efficient deployment and tasks benefiting from accelerated fine-tuning.
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Model Overview
The ertghiu256/Qwen3-1.7B-tiny-orchestrator is a 2 billion parameter language model based on the Qwen3 architecture, developed by ertghiu256. It was finetuned from the unsloth/qwen3-1.7b-unsloth-bnb-4bit base model, leveraging the Unsloth library in conjunction with Huggingface's TRL library.
Key Characteristics
- Architecture: Qwen3
- Parameter Count: 2 billion parameters
- Context Length: 32,768 tokens
- Training Efficiency: Achieved 2x faster training speeds due to the use of Unsloth.
- License: Apache-2.0
Use Cases
This model is particularly well-suited for applications where:
- Rapid Prototyping: The accelerated training process makes it ideal for quick experimentation and iteration.
- Resource-Constrained Environments: Its smaller parameter count (2B) combined with efficient training suggests suitability for deployment on less powerful hardware.
- Specific Fine-tuning Tasks: Developers looking to fine-tune a Qwen3-based model quickly for particular downstream tasks will benefit from its optimized training methodology.