ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint
The ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint is a 4 billion parameter Qwen3-based causal language model, developed by ermiaazarkhalili and fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is optimized for efficient performance in language generation tasks.
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Model Overview
This model, ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint, is a 4 billion parameter language model based on the Qwen3 architecture. It was developed by ermiaazarkhalili and fine-tuned from the unsloth/qwen3-4b-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen3-based causal language model.
- Parameter Count: 4 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which enabled 2x faster training compared to standard methods.
Potential Use Cases
Given its efficient training and Qwen3 foundation, this model is suitable for applications requiring:
- Text Generation: Creating coherent and contextually relevant text.
- Instruction Following: Responding to prompts and instructions effectively.
- Resource-Efficient Deployment: Its 4B parameter size, combined with Unsloth's optimization, makes it a candidate for scenarios where computational resources are a consideration.