u-lee/hkTestModel
TEXT GENERATIONConcurrency Cost:1Model Size:2.5BQuant:BF16Ctx Length:8kPublished:Apr 2, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The u-lee/hkTestModel is a 2.5 billion parameter instruction-tuned causal language model developed by u-lee, based on the Google Gemma 2 2B IT architecture. This model is primarily focused on testing and evaluation, utilizing the hyokwan/test_data dataset. It is designed for general language tasks with a context length of 8192 tokens, and its development includes a specific emphasis on the Korean language.
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Overview
The u-lee/hkTestModel is a 2.5 billion parameter instruction-tuned language model built upon the Google Gemma 2 2B IT architecture. Developed by u-lee, this model is specifically designed for testing and evaluation purposes, leveraging the hyokwan/test_data dataset.
Key Characteristics
- Base Model: Google Gemma 2 2B IT
- Parameter Count: 2.5 billion
- Context Length: 8192 tokens
- Primary Language Focus: Korean (
ko) - Training Data: Utilizes the
hyokwan/test_datadataset for its development and evaluation. - Metrics: Evaluated using accuracy metrics.
Use Cases
This model is primarily intended for:
- Testing and Experimentation: Ideal for developers and researchers to test new methodologies or evaluate model behavior within a controlled environment.
- Korean Language Processing: Its focus on the Korean language makes it suitable for tasks requiring understanding or generation in Korean.
- Benchmarking: Can be used as a baseline or comparison model in various language understanding and generation benchmarks, particularly for smaller, instruction-tuned models.