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DeepSeek-V2.5

Artificial intelligence model integrating general and programming capabilities

#Artificial Intelligence
#natural language processing
#machine learning
#Programming assistance
DeepSeek-V2.5

Product Details

DeepSeek-V2.5 is an upgraded version that combines the functions of DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. This new model integrates the general and programming capabilities of the two previous versions, better aligns with human preferences, and is optimized in multiple aspects such as writing and instruction following.

Main Features

1
Supports using Huggingface’s Transformers for model inference
2
It is recommended to use vLLM for model inference for better performance
3
Support function calling, the model can call external tools to enhance its capabilities
4
Supports JSON output mode to ensure valid JSON objects are generated
5
Supports FIM (Fill In the Middle) completion, providing prefixes and suffixes, and the model completes the intermediate content

How to Use

1
First, make sure Huggingface’s Transformers library is installed in your environment
2
Import necessary modules such as torch, AutoTokenizer and AutoModelForCausalLM
3
Use the AutoTokenizer.from_pretrained method to load the DeepSeek-V2.5 model
4
Set the model's generation configuration, such as the maximum number of new tokens, stop token ID, etc.
5
Prepare the input message and use tokenizer to convert the message into a format understandable by the model
6
Call the model's generate method to generate the response
7
Decode the generated response into a readable text format

Target Users

DeepSeek-V2.5 is suitable for developers and researchers who need artificial intelligence support in dialogue and programming tasks. It can understand complex instructions and generate corresponding code or dialogue, greatly improving development efficiency and user experience.

Examples

Use DeepSeek-V2.5 to generate C++ code for the quick sort algorithm

Use the model to call the external weather API to obtain weather information for a specific city

Ask model questions in conversations and get answers in structured JSON format

Quick Access

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