DeepSeek V2.5

DeepSeek-V2.5 is an advanced language model developed by DeepSeek AI. It combines the strengths of previous versions, enhancing both general and coding capabilities. With 236 billion parameters, it excels in tasks like text generation, coding, and instruction following. This model is designed for efficiency, providing economical training and fast inference.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "deepseek/deepseek-chat",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="deepseek/deepseek-chat",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

DeepSeek V2.5

Context65536

Input$0.14 / M

Output$0.28 / M

Try DeepSeek V2.5

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Description

DeepSeek V2.5 is a strong language model made by DeepSeek AI. It was released in September 2023. This model combines the best features of its earlier versions. These include DeepSeek V2-Chat and DeepSeek Coder V2-Instruct. 

DeepSeek V2.5 shines in text generation and coding tasks. It has shown great results in many tests. For example, it scored 50.5 in Alpaca Eval 2.0, beating older versions. In the Arenahard benchmark, it went from 68.3% to 76.2%. Its Align Bench score also rose from 7.88 to 8.04. 

The model has 236 billion parameters in its structure. During inference, 21 billion parameters are active for each token. This setup helps DeepSeek V2.5 use its resources well. It produces high-quality outputs as a result. 

In coding tasks, DeepSeek V2.5 performs very well. Its score on the Humaneval Python benchmark hit 89%. In the Live Code Bench, it improved from 36.6% to 41.8%. Users can create human-like texts and write code in different languages. It can also translate between languages easily.

The model has advanced features like function calling and JSON output. This makes DeepSeek V2.5 great for many uses. Its ability to match human preferences stands out. Users can fully use its potential for effective AI solutions. To get better pricing for integrating this amazing model, use our AIAPILAB services!

Model API Use Case

DeepSeek V2.5 is a strong API for text generation and coding tasks. It combines DeepSeek's chat and coder models. This version has 236 billion parameters. Users can create text that sounds human or write code in many programming languages like C++.

In coding, DeepSeek V2.5 scored 89% on the Humaneval benchmark. This shows it can generate accurate code snippets. Users can ask for specific tasks, like "write a piece of quick sort code in C++." The model gives back clear and efficient code.

When it comes to natural language processing, DeepSeek V2.5 shines in translation. It translates text from English to Chinese. This makes it easier for many users to access information. Its performance on the Alpaca Eval 2.0 benchmark went up from 46.6% to 50.5%. This means it aligns better with what humans prefer.

DeepSeek V2.5 also has a feature for calling functions. It can pull real-time data, such as weather updates. This makes it a useful tool for developers. The API is available for commercial use. It costs $0.14 for every million input tokens. For more details, check out [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V2.5).

Model Review

Pros

1. DeepSeek V2.5 merges coding and conversational skills, enhancing versatility. 2. It generates human-like text and writes code in multiple languages effectively. 3. The model shows remarkable improvements in benchmark tests, outperforming previous versions. 4. Users can leverage advanced features like function calling and JSON output seamlessly. 5. Its design optimizes resource use, ensuring efficient and high-quality outputs.

Cons

1. DeepSeek V2.5 demands significant hardware. Users need 80 GB across eight GPUs for local use. 2. The model struggles with complex function calling. Its feature is still in early development stages. 3. JSON output requires special instructions. Users may find it challenging to generate valid responses.

Comparison

Feature/Aspect	DeepSeek V2.5	DeepSeek V2.5-1210	Other Model (GPT-4)
Model Type	Language Model	Language Model	Language Model
Parameters	236 billion	236 billion	175 billion
JSON Output	Generates valid JSON objects	Improved JSON output capabilities	Generates JSON but with specific prompts
Function Calling	Supports external tool calls	Enhanced user experience for file uploads	Supports function calling
Performance on Coding Tasks	High performance on humaneval (89%)	Improved performance on math-500 (82.8%)	High performance on code generation

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "deepseek/deepseek-chat",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="deepseek/deepseek-chat",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1：How can I run DeepSeek V2.5 locally?  
A1：Use 80 GB and 8 GPUs. Follow the provided setup instructions.

Q2：What tasks can DeepSeek V2.5 perform?  
A2：It generates text, writes code, translates languages, and answers questions.

Q3：How do I access the API for DeepSeek V2.5?  
A3：Use the endpoint for model requests. Follow the API documentation for details.

Q4：Can DeepSeek V2.5 generate JSON output?  
A4：Yes, append special instructions to your prompt for valid JSON responses.

Q5：What is the recommended way to utilize DeepSeek V2.5 for inference?  
A5：Use Hugging Face's transformers or VLLM for efficient performance.