From cd36b4a0137490b31cfe535ba8cdb3869a44c457 Mon Sep 17 00:00:00 2001 From: YoungjuKim Date: Sat, 17 Oct 2026 03:53:56 +0900 Subject: [PATCH] [fix] decide native-vs-transformers loading by directory name, not substring "model" in args.load_from matched any path containing "model" (for example a Hugging Face cache path or /data/models/...), so the scripts loaded ./out/*.pth instead of the transformers checkpoint the user passed. Compare the last path component with "model" instead. Signed-off-by: YoungjuKim --- eval_llm.py | 3 ++- scripts/eval_toolcall.py | 2 +- scripts/serve_openai_api.py | 2 +- 3 files changed, 4 insertions(+), 3 deletions(-) diff --git a/eval_llm.py b/eval_llm.py index 6339711..2ef2e12 100755 --- a/eval_llm.py +++ b/eval_llm.py @@ -1,3 +1,4 @@ +import os import time import argparse import random @@ -11,7 +12,7 @@ warnings.filterwarnings('ignore') def init_model(args): tokenizer = AutoTokenizer.from_pretrained(args.load_from) - if 'model' in args.load_from: + if os.path.basename(os.path.normpath(args.load_from)) == 'model': model = MiniMindForCausalLM(MiniMindConfig( hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, diff --git a/scripts/eval_toolcall.py b/scripts/eval_toolcall.py index d159eff..a35e2d5 100644 --- a/scripts/eval_toolcall.py +++ b/scripts/eval_toolcall.py @@ -56,7 +56,7 @@ TEST_CASES = [ def init_model(args): tokenizer = AutoTokenizer.from_pretrained(args.load_from) - if 'model' in args.load_from: + if os.path.basename(os.path.normpath(args.load_from)) == 'model': model = MiniMindForCausalLM(MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe))) moe_suffix = '_moe' if args.use_moe else '' ckp = f'./{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth' diff --git a/scripts/serve_openai_api.py b/scripts/serve_openai_api.py index 09e7fc1..0da37bc 100644 --- a/scripts/serve_openai_api.py +++ b/scripts/serve_openai_api.py @@ -27,7 +27,7 @@ app = FastAPI() def init_model(args): tokenizer = AutoTokenizer.from_pretrained(args.load_from) - if 'model' in args.load_from: + if os.path.basename(os.path.normpath(args.load_from)) == 'model': moe_suffix = '_moe' if args.use_moe else '' ckp = f'../{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth' model = MiniMindForCausalLM(MiniMindConfig(