Piyush14123421/Qwen3-4B-Thinking
TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 6, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Piyush14123421/Qwen3-4B-Thinking is a 4 billion parameter Qwen3 model developed by Piyush14123421, fine-tuned from unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit. This model was trained significantly faster using Unsloth and Huggingface's TRL library, offering efficient performance for its size. With a 32768 token context length, it is suitable for tasks requiring substantial input processing.
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Model Overview
Piyush14123421/Qwen3-4B-Thinking is a 4 billion parameter language model based on the Qwen3 architecture. It was developed by Piyush14123421 and fine-tuned from the unsloth/Qwen3-4B-Thinking-2507-unsloth-bnb-4bit base model.
Key Characteristics
- Efficient Training: This model distinguishes itself by being trained approximately 2x faster through the integration of Unsloth and Huggingface's TRL library. This optimization allows for quicker iteration and deployment cycles.
- Context Length: It supports a substantial context window of 32768 tokens, enabling it to process and generate longer sequences of text, which is beneficial for complex tasks requiring extensive contextual understanding.
Potential Use Cases
- Rapid Prototyping: The model's efficient training methodology makes it suitable for developers looking to quickly fine-tune and deploy language models for specific applications.
- Applications Requiring Long Context: Its large context window is advantageous for tasks such as document summarization, detailed question answering, or conversational AI where maintaining long-term coherence is crucial.