U82-IA/Agent_4b
U82-IA/Agent_4b is a 4 billion parameter Qwen3-based instruction-tuned causal language model developed by U82-IA. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen3 architecture and efficient fine-tuning process.
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Overview
U82-IA/Agent_4b is a 4 billion parameter instruction-tuned model based on the Qwen3 architecture. Developed by U82-IA, this model was fine-tuned using a combination of Unsloth and Huggingface's TRL library. This approach allowed for a significantly faster training process, reportedly 2x quicker than standard methods.
Key Characteristics
- Base Model: Qwen3-4B-Instruct-2507-unsloth-bnb-4bit
- Parameter Count: 4 billion parameters
- Context Length: 32768 tokens
- Training Efficiency: Fine-tuned with Unsloth for accelerated training.
Use Cases
This model is suitable for a variety of general instruction-following applications, benefiting from its efficient training and Qwen3 foundation. Its 4 billion parameters make it a good candidate for tasks requiring a balance between performance and computational resources.