malhajar/Qwen1.5-7B-turkish
TEXT GENERATIONConcurrency Cost:1Model Size:7.7BQuant:FP8Ctx Length:32kLicense:apache-2.0Architecture:Transformer0.0K Open Weights Cold
The malhajar/Qwen1.5-7B-turkish model is a 7.7 billion parameter language model developed by Mohamad Alhajar, fine-tuned from Qwen1.5-7B. This model is specifically optimized for Turkish language processing and instruction-following tasks. It excels at generating conversational responses in Turkish, making it suitable for chat-based applications and information retrieval in Turkish.
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Overview
malhajar/Qwen1.5-7B-turkish is a 7.7 billion parameter language model developed by Mohamad Alhajar. It is a fine-tuned version of the Qwen1.5-7B base model, specifically adapted for the Turkish language. The fine-tuning process utilized SFT (Supervised Fine-Tuning) and the Freeze method, focusing on instruction-based learning.
Key Capabilities
- Turkish Language Proficiency: Optimized for understanding and generating text in Turkish.
- Instruction Following: Fine-tuned on the
alpaca-gpt4-trdataset, enabling it to respond effectively to instructions in a chat format. - Conversational AI: Designed to provide informative answers and engage in dialogue, making it suitable for interactive applications.
Good For
- Turkish Chatbots: Developing AI assistants or chatbots that communicate naturally in Turkish.
- Information Retrieval: Answering questions and providing information in Turkish based on given prompts.
- Turkish NLP Applications: Any application requiring robust Turkish language generation and comprehension capabilities, particularly in an instruction-following context.