gjyotin305/Qwen2.5-3B-Instruct_new_alpaca_005
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Jan 13, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
gjyotin305/Qwen2.5-3B-Instruct_new_alpaca_005 is a 3.1 billion parameter instruction-tuned causal language model, finetuned by gjyotin305 from unsloth/Qwen2.5-3B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and a 32768 token context length.
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Model Overview
This model, gjyotin305/Qwen2.5-3B-Instruct_new_alpaca_005, is a 3.1 billion parameter instruction-tuned language model. It was developed by gjyotin305 and is based on the Qwen2.5-3B-Instruct architecture, offering a substantial context length of 32768 tokens.
Key Characteristics
- Architecture: Built upon the Qwen2.5-3B-Instruct base model.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Instruction Following: Optimized for understanding and executing instructions, making it suitable for a variety of conversational and task-oriented applications.
Potential Use Cases
- General Instruction Following: Ideal for tasks requiring the model to respond to prompts and instructions.
- Conversational AI: Can be applied in chatbots or virtual assistants due to its instruction-tuned nature.
- Rapid Prototyping: The efficient training methodology suggests it could be a good candidate for projects requiring quick iteration and deployment of instruction-tuned models.