DeepSeek-V2-Chat-0628 represents the latest in AI advancements, particularly for those looking to enhance their chatbot applications. This guide will walk you through the steps to run this powerful model locally, along with troubleshooting tips for a seamless experience.

1. Introduction

DeepSeek-V2-Chat-0628 is an improved version, outshining its predecessor with remarkable performance on the LMSYS Chatbot Arena Leaderboard. It excels in coding and challenging prompts making it a valuable tool for developers.

2. How to Run DeepSeek-V2-Chat-0628

In order to effectively run DeepSeek-V2-Chat-0628 locally, you will need access to a powerful computing setup. Specifically, you will require:

• A machine with 8 GPUs, each with 80GB of memory.

2.1 Inference with Huggingface’s Transformers

You can leverage the Transformers library from Huggingface for model inference. The following steps outline the process:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/DeepSeek-V2-Chat-0628"
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)

This code showcases how to initialize and utilize the model for generating responses. Think of this process like baking a cake. Each ingredient (or line of code) is essential for creating the final product (the output). If you miss any ingredients, the cake won’t turn out right.

2.2 Inference with vLLM (Recommended)

To enhance performance, you can also use the vLLM framework for inference. Follow these steps:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 8192, 8
model_name = "deepseek-ai/DeepSeek-V2-Chat-0628"
tokenizer = AutoTokenizer.from_pretrained(model_name)

llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)

sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])

messages_list = [
    [{"role": "user", "content": "Who are you?"}],
    [{"role": "user", "content": "Translate the following content into Chinese directly: DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference."}],
    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

In this example, you can see how the new sampling parameters create more refined outputs, much like a chef adjusting spices to create a perfect flavor profile. Each message adds a unique element to the overall taste of the conversation.

3. Troubleshooting

If you encounter errors while following these steps, consider the following troubleshooting tips:

• Ensure that your hardware meets the requirements (8 GPUs with sufficient memory).
• Double-check that the version of PyTorch and Huggingface Transformers are compatible with DeepSeek-V2-Chat-0628.
• If there are issues with the model loading, verify your internet connection or permissions for accessing model files.
• Refer to the chat template in `tokenizer_config.json` for possible discrepancies.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

4. Conclusion

Utilizing DeepSeek-V2-Chat-0628 can greatly enhance your AI chatbot capabilities. With its improved performance and ease of use, you’re well-equipped to tackle various tasks efficiently. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.