Fatma04/Finetuned_Qwen3-4B-Egyptian-Model
The Fatma04/Finetuned_Qwen3-4B-Egyptian-Model is a 4 billion parameter Qwen3-based causal language model developed by Fatma04, fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language generation tasks with a 32768 token context length, leveraging its efficient fine-tuning process.
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Model Overview
This model, developed by Fatma04, is a 4 billion parameter Qwen3-based causal language model. It was fine-tuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit base model, indicating an instruction-following capability. The fine-tuning process utilized Unsloth and Huggingface's TRL library, which enabled a 2x faster training speed.
Key Characteristics
- Architecture: Qwen3-based, a robust and widely used LLM architecture.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended responses.
- Training Efficiency: Benefits from Unsloth's optimization, resulting in significantly faster fine-tuning.
Potential Use Cases
Given its instruction-tuned nature and efficient training, this model is suitable for a variety of applications, including:
- General Text Generation: Creating diverse forms of content, from creative writing to informative summaries.
- Instruction Following: Responding to prompts and performing tasks as directed by user instructions.
- Research and Development: Serving as a base for further experimentation and fine-tuning on specific datasets due to its optimized training.
This model is released under the Apache-2.0 license.