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README.md
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## Training
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The model was fine-tuned on a dataset translated into Javanese using the [NLLB](https://ai.meta.com/research/no-language-left-behind/) model. This dataset includes texts from both [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1) and [OASST2](https://huggingface.co/datasets/OpenAssistant/oasst2), covering a wide range of conversational scenarios. The training process employed [
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The following `bitsandbytes` quantization settings were applied during training:
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- quant_method: bitsandbytes
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: fp4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float32
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## Usage
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## Training
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The model was fine-tuned on a dataset translated into Javanese using the [NLLB](https://ai.meta.com/research/no-language-left-behind/) model. This dataset includes texts from both [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1) and [OASST2](https://huggingface.co/datasets/OpenAssistant/oasst2), covering a wide range of conversational scenarios. The training process employed [PEFT](https://github.com/huggingface/peft) and [TRL](https://github.com/huggingface/trl) to facilitate efficient and rapid fine-tuning.
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## Usage
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