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from pathlib import Path | |
# == Embeddings model == | |
EMBEDDING_MODEL_ID = "sentence-transformers/all-MiniLM-L6-v2" | |
EMBEDDING_MODEL_MAX_INPUT_LENGTH = 384 | |
# == VECTOR Database == | |
VECTOR_DB_OUTPUT_COLLECTION_NAME = "alpaca_financial_news" | |
VECTOR_DB_SEARCH_TOPK = 1 | |
# == LLM Model == | |
LLM_MODEL_ID = "unsloth/mistral-7b-instruct-v0.2-bnb-4bit" | |
LLM_QLORA_CHECKPOINT = "plantbased/mistral-7b-instruct-v0.2-4bit" | |
LLM_INFERNECE_MAX_NEW_TOKENS = 500 | |
LLM_INFERENCE_TEMPERATURE = 1.0 | |
# == Prompt Template == | |
TEMPLATE_NAME = "mistral" | |
# === Misc === | |
CACHE_DIR = Path.home() / ".cache" / "hands-on-llms" | |