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Model: DAMO-NLP-MT/polylm-multialpaca-13b Source: Original Platform
2026-08-09 13:19:16 +08:00
commit 692efb90ec
11 changed files with 1000 additions and 0 deletions
--- a/polylm_cli_demo.py
+++ b/polylm_cli_demo.py
@@ -0,0 +1,94 @@
+import argparse
+import os
+import platform
+import warnings
+
+import re
+pattern = re.compile("[\n]+")
+
+import torch
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+from huggingface_hub import snapshot_download
+
+from transformers.generation.utils import logger
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name", default="DAMO-NLP-MT/polylm-multialpaca-13b", 
+                    choices=["DAMO-NLP-MT/polylm-multialpaca-13b"], type=str)
+parser.add_argument("--multi_round", action="store_true",
+                    help="Turn multiple rounds interaction on.")
+parser.add_argument("--gpu", default="0", type=str)
+args = parser.parse_args()
+
+os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
+num_gpus = len(args.gpu.split(","))
+
+if args.model_name in ["DAMO-NLP-MT/polylm-multialpaca-13b-int8", "DAMO-NLP-MT/polylm-multialpaca-13b-int4"] and num_gpus > 1:
+    raise ValueError("Quantized models do not support model parallel. Please run on a single GPU (e.g., --gpu 0).")
+
+logger.setLevel("ERROR")
+warnings.filterwarnings("ignore")
+
+model_path = args.model_name
+if not os.path.exists(args.model_name):
+    model_path = snapshot_download(args.model_name)
+
+config = AutoConfig.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+
+if num_gpus > 1:  
+    print("Waiting for all devices to be ready, it may take a few minutes...")
+    with init_empty_weights():
+        raw_model = AutoModelForCausalLM.from_config(config)
+    raw_model.tie_weights()
+    model = load_checkpoint_and_dispatch(
+        raw_model, model_path, device_map="auto", no_split_module_classes=["GPT2Block"]
+    )
+else:
+    print("Loading model files, it may take a few minutes...")
+    model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").cuda()
+
+def clear():
+    os.system('cls' if platform.system() == 'Windows' else 'clear')
+    
+def main():
+    print("欢迎使用 PolyLM 多语言人工智能助手！输入内容即可进行对话。输入 clear 以清空对话历史，输入 stop 以终止对话。")
+    prompt = ""
+    while True:
+        query = input()
+        if query.strip() == "stop":
+            break
+        if query.strip() == "clear":
+            if args.multi_round:
+                prompt = ""
+            clear()
+            continue
+        
+        text = query.strip()
+        text = re.sub(pattern, "\n", text)
+        if args.multi_round:
+            prompt += f"{text}\n\n"
+        else:
+            prompt = f"{text}\n\n"
+        
+        inputs = tokenizer(prompt, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs.input_ids.cuda(), 
+                attention_mask=inputs.attention_mask.cuda(), 
+                max_length=1024, 
+                do_sample=True, 
+                top_p=0.8, 
+                temperature=0.7,
+                repetition_penalty=1.02,
+                num_return_sequences=1, 
+                eos_token_id=2,
+                early_stopping=True)
+            response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+            if args.multi_round:
+                prompt += f"{response}\n"
+            print(f">>> {response}")
+    
+if __name__ == "__main__":
+    main()