PS4Research/kE5nV8hA3yW4jT7s
TEXT GENERATIONConcurrency Cost:1Model Size:14BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:May 12, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
PS4Research/kE5nV8hA3yW4jT7s is a 14 billion parameter Qwen3-based language model developed by PS4Research, fine-tuned from unsloth/Qwen3-14B-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster finetuning. With a 32768 token context length, it is optimized for efficient deployment and performance in tasks leveraging the Qwen3 architecture.
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Model Overview
PS4Research/kE5nV8hA3yW4jT7s is a 14 billion parameter language model based on the Qwen3 architecture, developed by PS4Research. It was fine-tuned from the unsloth/Qwen3-14B-bnb-4bit model, leveraging the Unsloth library for accelerated training.
Key Characteristics
- Architecture: Qwen3-based, 14 billion parameters.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, resulting in a 2x speed improvement during the finetuning process.
- Context Length: Supports a context window of 32768 tokens.
- License: Released under the Apache-2.0 license, allowing for broad use and distribution.
Good For
- Efficient Deployment: Suitable for applications requiring a powerful Qwen3-based model that benefits from optimized finetuning techniques.
- Research and Development: Ideal for researchers and developers looking to build upon or experiment with efficiently trained Qwen3 models.
- Cost-Effective Finetuning: The use of Unsloth suggests an emphasis on reducing computational resources and time for model adaptation.