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Kolyadual
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Model Details
Model Provider
Kolyadual
Model Tree
mistralai/Mistral-Nemo-Instruct-2407
Input Modalities
Output Modalities
Supported Functionality
MiLana is a state-of-the-art 12-billion parameter language model, surgically merged to deliver the perfect balance of structured reasoning and unrestricted creativity. Built on the robust Mistral Nemo architecture, it is engineered to provide highly accurate, context-aware, and fluent responses, with exceptional native-level proficiency in both Russian and English.
Option 1: Hugging Face Transformers (Python) Ideal for direct integration into Python applications (like the Newton bot).
markdown
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmodel_path = "./MiLana" # Path to the extracted model foldertokenizer = AutoTokenizer.from_pretrained(model_path)model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.float16,device_map="auto" # Use "cpu" if no GPU is available)
Option 2: llama.cpp (GGUF Format) Ideal for low-resource environments, local desktop apps, or mobile deployment. The model is highly compatible with Q5_K_M or Q8_0 quantization, retaining 99% of its bfloat16 performance while drastically reducing RAM usage.
llama-cli -m MiLana_Q5_K_M.gguf
--system-prompt "You are Newton bot, a smart and helpful AI assistant for the Runget project."
--prompt "Hello! What can you do?"
--n-predict 256
--temp 0.7
RECOMMENDED SYSTEM PROMPT "You are MiLana, an advanced AI assistant integrated into the Newton bot ecosystem for the Runget operating system. You are highly capable, technically proficient, and direct. You provide accurate, well-structured, and concise answers. You excel at coding, system administration, and logical reasoning. Always prioritize the user's explicit instructions."