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feat: upgrade MiniMax default model to M3 #1859

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feat: upgrade MiniMax default model to M3 #1859

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6 changes: 3 additions & 3 deletions examples/basic_modules/llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,14 +167,14 @@
# Scenario 7: Using LLMFactory with MiniMax (OpenAI-compatible API)
# Prerequisites:
# 1. Get your API key from the MiniMax platform.
# 2. Available models: MiniMax-M2.7 (flagship), MiniMax-M2.7-highspeed (low-latency),
# MiniMax-M2.5, MiniMax-M2.5-highspeed.
# 2. Available models: MiniMax-M3 (flagship, default), MiniMax-M2.7,
# MiniMax-M2.7-highspeed (low-latency).

cfg_mm = LLMConfigFactory.model_validate(
{
"backend": "minimax",
"config": {
"model_name_or_path": "MiniMax-M2.7",
"model_name_or_path": "MiniMax-M3",
"api_key": "your-minimax-api-key",
"api_base": "https://api.minimax.io/v1",
"temperature": 0.7,
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2 changes: 1 addition & 1 deletion src/memos/api/config.py
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Expand Up @@ -288,7 +288,7 @@ def qwen_config() -> dict[str, Any]:
def minimax_config() -> dict[str, Any]:
"""Get MiniMax configuration."""
return {
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "MiniMax-M2.7"),
"model_name_or_path": os.getenv("MOS_CHAT_MODEL", "MiniMax-M3"),
"temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", "0.8")),
"max_tokens": int(os.getenv("MOS_MAX_TOKENS", "8000")),
"top_p": float(os.getenv("MOS_TOP_P", "0.9")),
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