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README.md
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# Maistros-8B-Instruct: A Greek Large Language Model adapted through Knowledge Distillation from Large Reasoning Models
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‼️If the full model does not fit in your setup, you can use the official [4-bit quantized version](https://huggingface.co/IMISLab/Maistros-8B-Instruct-4bit), which uses 65% less memory.
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We introduce Maistros-8B-Instruct, a Greek-adapted LLM based on `mistralai/Ministral-3-8B-Instruct-2512-BF16` fine-tuned using Low-Rank Adaptation (LoRA) on [CulturaQA](https://huggingface.co/datasets/IMISLab/CulturaQA).
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For information regarding the model training, validation and evaluation, as well as its limitations see the [arxiv preprint]().
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'right'
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# Load the model from the path to the device and set it in evaluation mode.
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self.model = Mistral3ForConditionalGeneration.from_pretrained(model_path, device_map = self.device, trust_remote_code = True)
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self.model.eval()
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# Set the system, instruction and user prompts.
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system_prompt = ''
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instruction_prompt = ''
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user_prompt = ''
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# Defining the message template.
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---
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# Maistros-8B-Instruct: A Greek Large Language Model adapted through Knowledge Distillation from Large Reasoning Models
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‼️If the full model does not fit in your setup, you can use the official [4-bit quantized version](https://huggingface.co/IMISLab/Maistros-8B-Instruct-4bit), which uses 65% less memory.‼️
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We introduce Maistros-8B-Instruct, a Greek-adapted LLM based on `mistralai/Ministral-3-8B-Instruct-2512-BF16` fine-tuned using Low-Rank Adaptation (LoRA) on [CulturaQA](https://huggingface.co/datasets/IMISLab/CulturaQA).
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For information regarding the model training, validation and evaluation, as well as its limitations see the [arxiv preprint]().
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.padding_side = 'right'
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# Load the model from the path to the device and set it in evaluation mode.
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self.model = Mistral3ForConditionalGeneration.from_pretrained(model_path, device_map = self.device, trust_remote_code = True)
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self.model.eval()
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# Set the system, instruction and user prompts.
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system_prompt = 'Είσαι ο Μαΐστρος, ένα εξαιρετικά ανεπτυγμένο μοντέλο Τεχνητής Νοημοσύνης για την Ελληνική γλώσσα.\nΈχεις δημιουργηθεί απο το IMIS Lab του Πανεπιστημιού Πατρών.'
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instruction_prompt = 'Παρακαλώ απάντησε στην παρακάτω απάντηση.'
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user_prompt = ''
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# Defining the message template.
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