Rislantrs/Gemma-2-Alpaca-Hukum-Indo
TEXT GENERATIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:16kPublished:May 13, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
Rislantrs/Gemma-2-Alpaca-Hukum-Indo is a 9 billion parameter instruction-tuned causal language model developed by Rislantrs, fine-tuned from unsloth/gemma-2-9b-it-bnb-4bit. This model leverages Unsloth and Huggingface's TRL library for accelerated training. With a context length of 16384 tokens, it is optimized for efficient performance and specific instruction-following tasks.
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Model Overview
Rislantrs/Gemma-2-Alpaca-Hukum-Indo is an instruction-tuned language model with 9 billion parameters and a context length of 16384 tokens. It was developed by Rislantrs and is based on the Gemma-2 architecture, specifically fine-tuned from the unsloth/gemma-2-9b-it-bnb-4bit model.
Key Characteristics
- Efficient Training: This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning compared to standard methods.
- Instruction Following: As an instruction-tuned model, it is designed to understand and execute commands based on given prompts.
- Gemma-2 Base: Benefits from the underlying capabilities and architecture of the Gemma-2 series.
Potential Use Cases
- Instruction-based tasks: Ideal for applications requiring the model to follow specific instructions or generate responses based on detailed prompts.
- Resource-efficient deployment: The use of Unsloth for training suggests potential for optimized performance and reduced resource consumption during fine-tuning and inference.