mwhyd2262/Alita-V4-Full-Merged
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Feb 16, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The mwhyd2262/Alita-V4-Full-Merged is a 14.8 billion parameter Qwen2.5-based instruction-tuned causal language model. Developed by mwhyd2262, it was finetuned using Unsloth and Huggingface's TRL library, resulting in faster training. This model is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture for robust performance.
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Overview
The mwhyd2262/Alita-V4-Full-Merged is a 14.8 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. It was developed by mwhyd2262 and finetuned from the unsloth/qwen2.5-14b-instruct-bnb-4bit model. A notable aspect of its development is the use of Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Capabilities
- Instruction Following: Designed to accurately follow a wide range of user instructions.
- Qwen2.5 Architecture: Benefits from the robust capabilities and performance characteristics of the Qwen2.5 base model.
- Efficient Training: Utilized Unsloth for accelerated finetuning, indicating potential for efficient deployment or further customization.
Good For
- General-purpose instruction-following applications.
- Tasks requiring a capable 14.8B parameter model with a 32K context length.
- Developers interested in models finetuned with Unsloth for performance and efficiency.