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@@ -21,7 +21,7 @@ tags:
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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]().
@@ -75,14 +75,13 @@ self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code = T
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  tokenizer.pad_token = tokenizer.eos_token
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  tokenizer.padding_side = 'right'
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-
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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.