noureldinayman/Qweb2.5-Aloe-Beta-Finetuned-50Steps-diff-rewardfunctions
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 5, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The noureldinayman/Qweb2.5-Aloe-Beta-Finetuned-50Steps-diff-rewardfunctions is a 7.6 billion parameter Qwen2.5-Aloe-Beta model, fine-tuned by noureldinayman. This model was trained 2x faster using Unsloth and Huggingface's TRL library, indicating an optimization for efficient fine-tuning. It is designed for general language generation tasks, leveraging its Qwen2.5 base for robust performance.
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Model Overview
This model, developed by noureldinayman, is a fine-tuned variant of the HPAI-BSC/Qwen2.5-Aloe-Beta-7B architecture. It features 7.6 billion parameters and was specifically optimized for faster training.
Key Characteristics
- Efficient Fine-tuning: The model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library, highlighting an emphasis on computational efficiency during the fine-tuning process.
- Base Model: Built upon the Qwen2.5-Aloe-Beta-7B foundation, suggesting strong general language understanding and generation capabilities.
Potential Use Cases
- Rapid Prototyping: Its efficient fine-tuning process makes it suitable for scenarios requiring quick iteration and deployment of specialized language models.
- General Language Tasks: Leveraging its Qwen2.5 base, it can be applied to a wide range of natural language processing applications.