deobfre/qwen_finetune_16bit
TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The deobfre/qwen_finetune_16bit is a 4 billion parameter Qwen3 instruction-tuned causal language model developed by deobfre. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for efficient deployment and inference, making it suitable for applications requiring a performant yet resource-conscious LLM.
Loading preview...
Model Overview
The deobfre/qwen_finetune_16bit is a 4 billion parameter Qwen3 instruction-tuned model, developed by deobfre. It was fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit using the Unsloth library and Huggingface's TRL, which significantly accelerated the training process by 2x.
Key Capabilities
- Efficient Fine-tuning: Leverages Unsloth for faster and more resource-efficient training.
- Qwen3 Architecture: Based on the Qwen3 model family, known for strong performance across various tasks.
- Instruction-Tuned: Designed to follow instructions effectively, making it suitable for conversational AI and task-oriented applications.
- Optimized for Deployment: The 16-bit finetuning and Unsloth optimization suggest a focus on practical, deployable performance.
Good For
- Resource-Constrained Environments: Its efficient training and 4B parameter size make it a good candidate for applications where computational resources are limited.
- Instruction Following Tasks: Ideal for chatbots, virtual assistants, and other applications requiring precise adherence to user prompts.
- Rapid Prototyping: The faster training facilitated by Unsloth can accelerate development cycles for custom LLM applications.
- Further Fine-tuning: Serves as a solid base model for additional domain-specific fine-tuning.