ianncity/glm4.7-sft
ianncity/glm4.7-sft is a 4 billion parameter Qwen3-based causal language model developed by ianncity, fine-tuned from unsloth/Qwen3-4B-Thinking-2507. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training speeds. With a 40960 token context length, it is optimized for efficient processing and generation tasks.
Loading preview...
Model Overview
The ianncity/glm4.7-sft is a 4 billion parameter language model, fine-tuned by ianncity. It is based on the Qwen3 architecture, specifically originating from the unsloth/Qwen3-4B-Thinking-2507 model.
Key Characteristics
- Architecture: Qwen3-based, a powerful causal language model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Features a substantial 40960 token context window, enabling the processing of longer inputs and generating more coherent, extended outputs.
- Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
Potential Use Cases
Given its efficient training and substantial context length, this model is suitable for applications requiring:
- Text Generation: Creating coherent and contextually relevant text over longer passages.
- Summarization: Handling lengthy documents or conversations for concise summaries.
- Question Answering: Processing extensive context to extract precise answers.
- Applications requiring efficient deployment: Its 4B parameter size, combined with optimized training, suggests it could be a good candidate for scenarios where faster inference or reduced resource consumption is beneficial.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.