Lopato4ka/qwen2.5-3b-gec-sft-merged
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 4, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Lopato4ka/qwen2.5-3b-gec-sft-merged is a 3.1 billion parameter Qwen2.5-based causal language model developed by Lopato4ka. Fine-tuned from unsloth/qwen2.5-3b-instruct-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library for accelerated performance. It is designed for general language tasks, leveraging its efficient training methodology.
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Model Overview
Lopato4ka/qwen2.5-3b-gec-sft-merged is a 3.1 billion parameter language model based on the Qwen2.5 architecture. Developed by Lopato4ka, this model was fine-tuned from unsloth/qwen2.5-3b-instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based causal language model.
- Parameter Count: 3.1 billion parameters.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library, enabling 2x faster training compared to standard methods.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
- General Text Generation: Suitable for a wide range of language generation tasks due to its foundational Qwen2.5 architecture.
- Efficient Deployment: Its optimized training process suggests potential for efficient inference, making it suitable for applications where resource constraints are a consideration.
- Further Fine-tuning: Can serve as a strong base model for additional fine-tuning on specific downstream tasks, benefiting from its efficient training heritage.