Witllm/qwen/demo.py

94 lines
3.1 KiB
Python

import torch
from modelscope import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from transformers import AutoConfig
from modeling_qwen import QWenLMHeadModel
seed = 4321
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
config, kwargs = AutoConfig.from_pretrained(
"./",
return_unused_kwargs=True,
trust_remote_code=True,
code_revision=None,
_commit_hash=None,
)
model = QWenLMHeadModel(config)
print(model)
# QWenLMHeadModel(
# (transformer): QWenModel(
# (wte): Embedding(151936, 2048)
# (drop): Dropout(p=0.0, inplace=False)
# (rotary_emb): RotaryEmbedding()
# (h): ModuleList(
# (0-23): 24 x QWenBlock(
# (ln_1): RMSNorm()
# (attn): QWenAttention(
# (c_attn): Linear(in_features=2048, out_features=6144, bias=True)
# (c_proj): Linear(in_features=2048, out_features=2048, bias=False)
# (attn_dropout): Dropout(p=0.0, inplace=False)
# )
# (ln_2): RMSNorm()
# (mlp): QWenMLP(
# (w1): Linear(in_features=2048, out_features=5504, bias=False)
# (w2): Linear(in_features=2048, out_features=5504, bias=False)
# (c_proj): Linear(in_features=5504, out_features=2048, bias=False)
# )
# )
# )
# (ln_f): RMSNorm()
# )
# (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
# )
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = model.from_pretrained(model_dir, config=config, device_map="auto", trust_remote_code=True)
# model = model.eval()
model = model.train() # control by @torch.no_grad()
# 可指定不同的生成长度、top_p等相关超参
# model.generation_config = GenerationConfig.from_pretrained(
# model_dir, trust_remote_code=True
# )
# 第一轮对话
response, history, decode_tokens = model.chat(tokenizer, "东南亚国家日本的首都是什么市", "", history=None)
print(decode_tokens)
# <|im_start|>system
# You are a helpful assistant.<|im_end|>
# <|im_start|>user
# 东南亚国家日本的首都是什么市<|im_end|>
# <|im_start|>assistant
# 日本的首都东京。<|im_end|><|endoftext|>
# # 第一轮对话
# response, history, decode_tokens = model.chat(tokenizer, "你好", "", history=None)
# print(decode_tokens)
# # 你好!很高兴为你提供帮助。
# 第二轮对话
response, history, decode_tokens = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "", history=None)
print(response)
# <|im_start|>system
# You are a helpful assistant.<|im_end|>
# <|im_start|>user
# 你好<|im_end|>
# <|im_start|>assistant
# 莎士比亚是头一个使用“你好”这个词的文学家,他在《哈姆雷特》中写道:“你是谁?你在哪儿?
# ”他的这一段话,通常被认为是最早的使用“你好”这个词的文学记载。这句话在英国语中非常常见,
# 特别是在正式或礼貌的情况下。<|im_end|><|endoftext|>