Raoufsebaoun/qwen2.5-islamic2
TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jun 19, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Raoufsebaoun/qwen2.5-islamic2 is a 7.6 billion parameter Qwen2.5-based causal language model developed by Raoufsebaoun, fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit. This model was optimized for faster training using Unsloth and Huggingface's TRL library, offering a 32768 token context length. It is designed for general instruction-following tasks, leveraging its efficient fine-tuning process.
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Model Overview
Raoufsebaoun/qwen2.5-islamic2 is a 7.6 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by Raoufsebaoun, this model was fine-tuned from unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit.
Key Capabilities
- Efficient Fine-tuning: This model was trained significantly faster (2x) by utilizing Unsloth and Huggingface's TRL library, making it a good example of efficient model adaptation.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute a wide range of user prompts and commands.
- Context Length: It supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Good For
- Developers looking for an efficiently fine-tuned Qwen2.5-based model.
- Applications requiring a capable instruction-following model with a large context window.
- Experimentation with models optimized using Unsloth for faster training.