From 08ce3da2289b0d7bf617f4a0057ac629ee229a6a Mon Sep 17 00:00:00 2001 From: jingyaogong Date: Thu, 30 Oct 2025 10:48:31 +0800 Subject: [PATCH] [feat] update args --- eval_llm.py | 2 +- scripts/serve_openai_api.py | 11 ++++++----- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/eval_llm.py b/eval_llm.py index 222c3ed..8af17d4 100755 --- a/eval_llm.py +++ b/eval_llm.py @@ -11,7 +11,7 @@ warnings.filterwarnings('ignore') def init_model(args): tokenizer = AutoTokenizer.from_pretrained(args.load_from) - if args.load_from == 'model': + if 'model' in args.load_from: model = MiniMindForCausalLM(MiniMindConfig( hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, diff --git a/scripts/serve_openai_api.py b/scripts/serve_openai_api.py index bbb4dec..76c8d8e 100644 --- a/scripts/serve_openai_api.py +++ b/scripts/serve_openai_api.py @@ -25,15 +25,16 @@ app = FastAPI() def init_model(args): - tokenizer = AutoTokenizer.from_pretrained('../model/') - if args.load_from == 'model': + tokenizer = AutoTokenizer.from_pretrained(args.load_from) + if 'model' in args.load_from: moe_suffix = '_moe' if args.use_moe else '' ckp = f'../{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth' model = MiniMindForCausalLM(MiniMindConfig( hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, max_seq_len=args.max_seq_len, - use_moe=bool(args.use_moe) + use_moe=bool(args.use_moe), + inference_rope_scaling=args.inference_rope_scaling )) model.load_state_dict(torch.load(ckp, map_location=device), strict=True) if args.lora_weight != 'None': @@ -41,7 +42,6 @@ def init_model(args): load_lora(model, f'../{args.save_dir}/lora/{args.lora_weight}_{args.hidden_size}.pth') else: model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True) - tokenizer = AutoTokenizer.from_pretrained(args.load_from) print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters()) / 1e6:.2f} M(illion)') return model.eval().to(device), tokenizer @@ -161,7 +161,7 @@ async def chat_completions(request: ChatRequest): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Server for MiniMind") - parser.add_argument('--load_from', default='model', type=str, help="模型加载路径（model=原生torch权重，其他路径=transformers格式）") + parser.add_argument('--load_from', default='../model', type=str, help="模型加载路径（model=原生torch权重，其他路径=transformers格式）") parser.add_argument('--save_dir', default='out', type=str, help="模型权重目录") parser.add_argument('--weight', default='full_sft', type=str, help="权重名称前缀（pretrain, full_sft, dpo, reason, ppo_actor, grpo, spo）") parser.add_argument('--lora_weight', default='None', type=str, help="LoRA权重名称（None表示不使用，可选：lora_identity, lora_medical）") @@ -169,6 +169,7 @@ if __name__ == "__main__": parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量（Small/MoE=8, Base=16）") parser.add_argument('--max_seq_len', default=8192, type=int, help="最大序列长度") parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）") + parser.add_argument('--inference_rope_scaling', default=False, action='store_true', help="启用RoPE位置编码外推（4倍，仅解决位置编码问题）") parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备") args = parser.parse_args() device = args.device