duttaturja/MLPredic-4B
The duttaturja/MLPredic-4B is a 4 billion parameter Qwen3 causal language model, developed by duttaturja and fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It features a 40960 token context length, making it suitable for applications requiring efficient processing of long sequences.
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Overview
The duttaturja/MLPredic-4B is a 4 billion parameter language model based on the Qwen3 architecture. Developed by duttaturja, this model was fine-tuned from unsloth/qwen3-4b-unsloth-bnb-4bit and leverages the Unsloth library in conjunction with Huggingface's TRL library for training. A key characteristic of its development is the reported 2x faster training speed achieved through the use of Unsloth.
Key Characteristics
- Model Architecture: Qwen3
- Parameter Count: 4 billion parameters
- Context Length: Supports a substantial 40960 tokens, enabling the processing of extensive inputs.
- Training Efficiency: Benefits from Unsloth for significantly accelerated training.
- License: Released under the Apache-2.0 license.
Good For
This model is particularly well-suited for use cases where:
- Efficient deployment of a 4B parameter Qwen3 model is desired.
- Applications requiring a large context window (40960 tokens) are critical.
- Developers are looking for a model fine-tuned with accelerated training techniques like Unsloth.