SeeWye/qwen_finetune1_16bit
The SeeWye/qwen_finetune1_16bit is a 4.5 billion parameter causal language model developed by SeeWye, fine-tuned from SeeWye/qwen3_5_TM_ocr_merged2. It features a 32768-token context length and has been optimized with SFT for rough alignment on output schema. This model is designed for tasks requiring structured output generation, leveraging efficient training methods.
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Overview
SeeWye/qwen_finetune1_16bit is a 4.5 billion parameter language model developed by SeeWye, fine-tuned from the SeeWye/qwen3_5_TM_ocr_merged2 base model. It is designed with a 32768-token context length and has undergone Supervised Fine-Tuning (SFT) to achieve rough alignment on specific output schemas. This makes it particularly suitable for applications where structured and predictable output formats are crucial.
Key Capabilities
- Schema-aligned Output: Optimized through SFT to produce outputs that conform to a predefined schema, enhancing reliability for structured data generation tasks.
- Efficient Training: The model was trained using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process.
- Qwen3.5 Base: Built upon the Qwen3.5 architecture, inheriting its foundational language understanding and generation capabilities.
Good For
- Structured Data Extraction: Ideal for tasks requiring the model to extract information and format it according to a specific JSON, XML, or other structured schema.
- Automated Content Generation: Useful in scenarios where generated text needs to adhere to a consistent format, such as report generation or automated responses.
- Applications requiring predictable output: Suitable for integration into systems that depend on consistent and parseable model responses.