Yusnia/qwen2.5-bfgai-labor-id
TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 16, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Yusnia/qwen2.5-bfgai-labor-id is a 7.6 billion parameter Qwen2.5-based causal language model developed by Yusnia, fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its Qwen2.5 architecture and efficient fine-tuning process.
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Model Overview
Yusnia/qwen2.5-bfgai-labor-id is a 7.6 billion parameter language model based on the Qwen2.5 architecture. It was developed by Yusnia and fine-tuned from the unsloth/Qwen2.5-7B-Instruct-bnb-4bit model.
Key Characteristics
- Architecture: Qwen2.5-based, a powerful causal language model family.
- Parameter Count: 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Potential Use Cases
This model is suitable for a variety of general natural language processing tasks, including:
- Text generation and completion.
- Instruction following and conversational AI.
- Summarization and information extraction.
- Educational applications and research in efficient model fine-tuning.