Jaahnak/hinglish-coder
TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:May 27, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Jaahnak/hinglish-coder is a 0.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by Jaahnak. Fine-tuned from unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit, this model was trained using Unsloth for accelerated performance. It is designed for general instruction-following tasks, leveraging its compact size and efficient training methodology.
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Jaahnak/hinglish-coder: A Compact Qwen2.5 Instruction Model
Jaahnak/hinglish-coder is a 0.5 billion parameter instruction-tuned language model built upon the Qwen2.5 architecture. Developed by Jaahnak, this model was fine-tuned from unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Based on the Qwen2.5 family of models.
- Parameter Count: Features 0.5 billion parameters, making it a relatively compact model suitable for resource-constrained environments.
- Training Efficiency: The model's training process was significantly accelerated using Unsloth, a library known for speeding up fine-tuning of large language models.
- Context Length: Supports a context length of 32768 tokens, allowing it to process substantial amounts of input information.
Potential Use Cases
- Instruction Following: Designed to respond to and execute instructions effectively.
- Edge Devices/Local Deployment: Its smaller size makes it a candidate for deployment on devices with limited computational resources.
- Rapid Prototyping: The efficient training process suggests it could be useful for quick experimentation and development cycles.