taskmaster141/qwen3_4b_merged-4000-txt
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The taskmaster141/qwen3_4b_merged-4000-txt is a 4 billion parameter Qwen3 instruction-tuned causal language model developed by taskmaster141. This model was finetuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit, leveraging Unsloth and Huggingface's TRL library for accelerated training. It is designed for general language understanding and generation tasks, benefiting from its efficient training methodology.
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Model Overview
The taskmaster141/qwen3_4b_merged-4000-txt is a 4 billion parameter Qwen3-based instruction-tuned language model. Developed by taskmaster141, this model was finetuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: The model was trained significantly faster (2x) by utilizing the Unsloth library in conjunction with Huggingface's TRL library.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more extensive outputs.
Potential Use Cases
This model is suitable for a variety of natural language processing tasks, including:
- Instruction following and response generation.
- Text summarization and content creation.
- Chatbot development and conversational AI.
- Applications requiring efficient inference due to its optimized training and moderate parameter count.