Ichsan2895/Merak-7B-v2
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Aug 6, 2023License:cc-by-nc-sa-4.0Architecture:Transformer0.0K Open Weights Cold
Ichsan2895/Merak-7B-v2 is a 7 billion parameter large language model developed by Muhammad Ichsan, fine-tuned from Meta Llama-2-7B-Chat-HF. Optimized for the Indonesian language, this model leverages QLoRA for efficient operation, requiring approximately 16 GB VRAM. It is specifically trained on Indonesian Wikipedia articles, making it highly proficient in generating and understanding Indonesian text.
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Merak-7B-v2: An Indonesian Language LLM
Merak-7B-v2 is a 7 billion parameter large language model developed by Muhammad Ichsan, specifically designed for the Indonesian language. It is built upon the Meta Llama-2-7B-Chat-HF architecture and has been fine-tuned using QLoRA for efficient performance, enabling it to run with 16 GB VRAM.
Key Capabilities
- Indonesian Language Proficiency: The model is extensively fine-tuned on a dataset of 600,000 Indonesian Wikipedia articles, enhancing its understanding and generation capabilities in Bahasa Indonesia.
- Efficient Deployment: Utilizes QLoRA (Quantized LoRA) for efficient fine-tuning and inference, making it accessible on systems with limited VRAM (e.g., 16 GB).
- Prompt-Style Adaptation: The v2 iteration incorporates changes in prompt-style during fine-tuning, building upon the initial Merak-7B model which was trained on 200,000 Indonesian Wikipedia articles.
Good For
- Applications requiring strong performance in the Indonesian language.
- Researchers and developers working on Indonesian NLP tasks.
- Environments with VRAM constraints that benefit from 4-bit quantization, though higher VRAM is recommended for optimal answer quality.