zycalice/qwen-orig-chem-sof-attention
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Feb 11, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The zycalice/qwen-orig-chem-sof-attention is a 32.8 billion parameter Qwen2 model, developed by zycalice, and finetuned from unsloth/Qwen2.5-32B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general language tasks, leveraging its large parameter count and efficient training methodology.
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Model Overview
The zycalice/qwen-orig-chem-sof-attention is a 32.8 billion parameter Qwen2 model, developed by zycalice. It was finetuned from the unsloth/Qwen2.5-32B-Instruct base model, indicating a focus on instruction-following capabilities.
Key Characteristics
- Architecture: Based on the Qwen2 model family.
- Parameter Count: Features 32.8 billion parameters, providing substantial capacity for complex language understanding and generation tasks.
- Training Efficiency: The model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods. This suggests an optimized and efficient development approach.
- Context Length: Supports a context length of 131,072 tokens, allowing it to process and generate very long sequences of text.
Potential Use Cases
- General Instruction Following: Suitable for a wide range of tasks that require understanding and responding to specific instructions.
- Applications Requiring Large Context: Its extensive context window makes it well-suited for tasks involving long documents, detailed conversations, or complex code analysis.
- Research and Development: The efficient training methodology could make it a valuable base for further finetuning or experimentation in various NLP domains.