DeepSeek-V2-Chat-0628 is an exceptional AI model designed to enhance your chatbot experience. Ever wondered how to run it locally or troubleshoot common issues? Look no further! This guide will walk you through every step, ensuring you can harness the power of DeepSeek-V2-Chat-0628 seamlessly.

Introduction to DeepSeek-V2-Chat-0628

Before diving into the usage, let’s get acquainted with the features of the DeepSeek-V2-Chat-0628 model. It’s a notable improvement over its predecessor, achieving impressive rankings in various coding challenges:

– Overall Ranking on LMSYS Chatbot Arena: #11
– Coding Arena Ranking: #3
– Hard Prompts Arena Ranking: #3

These achievements highlight its exceptional capabilities, especially in coding tasks. But how exactly do you set it up and start using it?

How to Run DeepSeek-V2-Chat-0628 Locally

Running DeepSeek-V2-Chat-0628 is like hosting a multi-course dinner. You need the right ingredients (hardware) and the perfect recipe (code) to serve up delicious results. To get started, you’ll need:

Hardware Requirements

– 8 GPUs with 80GB VRAM each for optimal performance.

Inference Using Huggingface’s Transformers

1. Install Required Libraries:
Ensure you have Huggingface’s Transformers library installed in your environment.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

# Load the model and tokenizer
model_name = "deepseek-ai/DeepSeek-V2-Chat-0628"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Set max memory based on your devices
max_memory = {i: "75GB" for i in range(8)}

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    device_map="sequential",
    torch_dtype=torch.bfloat16,
    max_memory=max_memory,
    attn_implementation="eager"
)

# Configure generation settings
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id

# Prepare user messages
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")

# Generate the output
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 is your cosmic power-up, allowing you to send requests to the model and receive spiffy AI-generated responses. Just like a well-oiled machine, each component works together to convert your inputs into engaging outputs.

Using vLLM for Inference (Recommended)

For a more optimized setup, you can use vLLM. The process is akin to using a turbocharger on a car—it enhances performance!

1. Merge the Required Pull Request in the vLLM codebase.

2. Use the Following Code:

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 this into Chinese: DeepSeek-V2 is innovative."}],
    [{"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)

The above code snippet effectively allows you to speak with the model and receive fluent conversational outputs, which feels like chatting with an old friend who just happens to know a lot!

Troubleshooting Tips

Even the best plans can hit a snag. Here are some common troubleshooting steps:

– Memory Issues: If you encounter memory-related errors, ensure that your GPUs are adequately equipped (75GB for each in use).
– Model Load Errors: Check if the model name is correctly specified and that all necessary libraries are installed.
– Unexpected Outputs: Review your input format. The message structure needs to align with the chat template provided.

For more troubleshooting questions/issues, contact our fxis.ai data scientist expert team.

Conclusion

DeepSeek-V2-Chat-0628 is a powerful tool for anyone delving into AI and chatbots. With the right setup and a few troubleshooting tips, you can make the most of this cutting-edge model. Enjoy your journey into the world of AI-assisted chatting!