myfi/parser_model_ner_4.00
The myfi/parser_model_ner_4.00 is a 4 billion parameter instruction-tuned causal language model developed by myfi, finetuned from unsloth/Qwen3-4B-Instruct-2507. It supports a context length of 32768 tokens and was trained using Unsloth and Huggingface's TRL library for accelerated finetuning. This model is optimized for specific parsing and Named Entity Recognition (NER) tasks, leveraging its Qwen3 base for efficient text processing.
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Overview
myfi/parser_model_ner_4.00 is a 4 billion parameter instruction-tuned language model developed by myfi. It is finetuned from the unsloth/Qwen3-4B-Instruct-2507 base model, leveraging the Qwen3 architecture. This model was trained with significant speed improvements using the Unsloth library and Huggingface's TRL library, enabling faster iteration and deployment.
Key Capabilities
- Efficient Finetuning: Benefits from Unsloth's optimizations for 2x faster training.
- Qwen3 Architecture: Inherits the robust capabilities of the Qwen3 model family.
- Extended Context: Supports a substantial context length of 32768 tokens, suitable for processing longer documents.
Good For
- Named Entity Recognition (NER): Designed for tasks requiring the identification and classification of entities within text.
- Text Parsing: Ideal for applications that involve structured extraction of information from unstructured text.
- Resource-Efficient Deployment: Its 4B parameter size, combined with Unsloth's training efficiency, makes it suitable for scenarios where faster training and inference are desired without sacrificing significant performance.