longtermrisk/Qwen3-4B-Instruct-2507-ftjob-c6534a30ef1e
The longtermrisk/Qwen3-4B-Instruct-2507-ftjob-c6534a30ef1e is a 4 billion parameter instruction-tuned causal language model developed by longtermrisk. Fine-tuned from unsloth/Qwen3-4B-Instruct-2507, this model was trained using Unsloth and Huggingface's TRL library, emphasizing faster training. It is designed for general instruction-following tasks, leveraging its 32768 token context length for processing longer inputs.
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Model Overview
This model, longtermrisk/Qwen3-4B-Instruct-2507-ftjob-c6534a30ef1e, is a 4 billion parameter instruction-tuned language model developed by longtermrisk. It is a finetuned version of unsloth/Qwen3-4B-Instruct-2507, leveraging the Qwen3 architecture.
Key Characteristics
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling it to handle longer and more complex prompts.
- Training Efficiency: The model was finetuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
Intended Use Cases
This model is suitable for a variety of instruction-following applications where a moderately sized, efficient language model with a good context window is beneficial. Its instruction-tuned nature makes it adept at understanding and executing user commands across different tasks.