# LLM_MODEL, see dbgpt/configs/model_config.LLM_MODEL_CONFIG
LLM_MODEL=tongyi_proxyllm
## LLM model path, by default, DB-GPT will read the model path from LLM_MODEL_CONFIG based on the LLM_MODEL.
## Of course you can specify your model path according to LLM_MODEL_PATH
## In DB-GPT, the priority from high to low to read model path:
## 1. environment variable with key: {LLM_MODEL}_MODEL_PATH (Avoid multi-model conflicts)
## 2. environment variable with key: MODEL_PATH
## 3. environment variable with key: LLM_MODEL_PATH
## 4. the config in dbgpt/configs/model_config.LLM_MODEL_CONFIG
# LLM_MODEL_PATH=/app/models/glm-4-9b-chat
# LLM_PROMPT_TEMPLATE=vicuna_v1.1
MODEL_SERVER=http://127.0.0.1:8000
LIMIT_MODEL_CONCURRENCY=5
MAX_POSITION_EMBEDDINGS=4096
QUANTIZE_QLORA=True
QUANTIZE_8bit=True
# QUANTIZE_4bit=False
## SMART_LLM_MODEL - Smart language model (Default: vicuna-13b)
## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
# SMART_LLM_MODEL=vicuna-13b
# FAST_LLM_MODEL=chatglm-6b
## Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm", When we use the rest API provided by deployment frameworks like fastchat as a proxyllm,
## "PROXYLLM_BACKEND" is the model they actually deploy. We can use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
PROXYLLM_BACKEND = qwen-turbo
### You can configure parameters for a specific model with {model name}_{config key}=xxx
### This option determines the storage location of conversation records. The default is not configured to the old version of duckdb. It can be optionally db or file (if the value is db, the database configured by LOCAL_DB will be used)