akshayballal/Qwen3-4B-Instruct-2507-SFT-Pubmed
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Jan 31, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The akshayballal/Qwen3-4B-Instruct-2507-SFT-Pubmed is a 4 billion parameter instruction-tuned Qwen3 model developed by akshayballal. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is designed for general instruction-following tasks, leveraging its Qwen3 architecture for efficient performance.
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Model Overview
The akshayballal/Qwen3-4B-Instruct-2507-SFT-Pubmed is a 4 billion parameter instruction-tuned model based on the Qwen3 architecture. It was developed by akshayballal and fine-tuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Qwen3-based, a causal language model.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
- Context Length: Supports a context length of 40960 tokens, allowing for processing of substantial input sequences.
Good For
- General instruction-following applications where a 4B parameter model is suitable.
- Scenarios requiring efficient inference due to its optimized training and moderate size.
- Developers looking for a Qwen3-based model with a focus on instruction-tuning.