服务器部署问题

[airsxue]

trafficstars

请问我用这个demo在linux运行v2需要安装哪些环境？谢谢 import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Chat" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

max_memory should be set based on your devices

max_memory = {i: "75GB" for i in range(8)}

device_map cannot be set to auto

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id

messages = [ {"role": "user", "content": "Write a piece of quicksort code in C++"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)

result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result)

[haichuan1221]

请问我用这个demo在linux运行v2需要安装哪些环境？谢谢 import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Chat" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

max_memory should be set based on your devices

max_memory = {i: "75GB" for i in range(8)}

device_map cannot be set to auto

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id

messages = [ {"role": "user", "content": "Write a piece of quicksort code in C++"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)

result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result)

同问

[stack-heap-overflow]

需要安装的python库包括：torch，transformers，accelerate。

相关库不同版本的兼容性并没有详细测过，这里可以给一个我测试用的环境供参考，不一定需要严格符合：

• torch == 2.1.0
• transformers == 4.39.3
• accelerate == 0.29.3