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DeepSeek-Coder-V2/README.md
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<p align="center">
<a href="#2-model-downloads">Model Download</a> |
<a href="#3-evaluation-results">Evaluation Results</a> |
<a href="#5-api-platform">API Platform</a> |
<a href="#6-how-to-run-locally">How to Use</a> |
<a href="#7-license">License</a> |
<a href="#8-citation">Citation</a>
</p>
<p align="center">
<a href="https://arxiv.org/pdf/2406.11931"><b>Paper Link</b>👁️</a>
</p>
# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
## 1. Introduction
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
<p align="center">
<img width="100%" src="figures/performance.png">
</p>
In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](supported_langs.txt).
## 2. Model Downloads
We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
<div align="center">
| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
</div>
## 3. Evaluation Results
### 3.1 Code Generation
| | #TP | #AP | HumanEval | MBPP+ | LiveCodeBench | USACO |
|:------------|:--------:|:--------:|:--------:|:--------:|:--------:|:-----------:|
| **Closed-Source Models** | | | | | | |
| **Gemini-1.5-Pro** | - | - | 83.5 | **74.6** | 34.1 | 4.9 |
| **Claude-3-Opus** | - | - | 84.2 | 72.0 | 34.6 | 7.8 |
| **GPT-4-Turbo-1106** | - | - | 87.8 | 69.3 | 37.1 | 11.1 |
| **GPT-4-Turbo-0409** | - | - | 88.2 | 72.2 | **45.7** | 12.3 |
| **GPT-4o-0513** | - | - | **91.0** | 73.5 | 43.4 | **18.8** |
| **Open-Source Models** | | | | | | |
| **CodeStral** | 22B | 22B | 78.1 | 68.2 | 31.0 | 4.6 |
| **DeepSeek-Coder-Instruct** | 33B | 33B | 79.3 | 70.1 | 22.5 | 4.2 |
| **Llama3-Instruct** | 70B | 70B | 81.1 | 68.8 | 28.7 | 3.3 |
| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 81.1 | 68.8 | 24.3 | 6.5 |
| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **90.2** | **76.2** | **43.4** | **12.1** |
### 3.2 Code Completion
| Model | #TP | #AP | RepoBench (Python) | RepoBench (Java) | HumanEval FIM |
| :------------------------------ | :--: | :--: | :----------------: | :--------------: | :-----------: |
| **CodeStral** | 22B | 22B | **46.1** | **45.7** | 83.0 |
| **DeepSeek-Coder-Base** | 7B | 7B | 36.2 | 43.3 | 86.1 |
| **DeepSeek-Coder-Base** | 33B | 33B | 39.1 | 44.8 | **86.4** |
| **DeepSeek-Coder-V2-Lite-Base** | 16B | 2.4B | 38.9 | 43.3 | **86.4** |
### 3.3 Code Fixing
| | #TP | #AP | Defects4J | SWE-Bench | Aider |
| ----------------------------------- | :--: | :--: | :-------: | :-------: | :------: |
| **Closed-Source Models** | | | | | |
| **Gemini-1.5-Pro** | - | - | 18.6 | 19.3 | 57.1 |
| **Claude-3-Opus** | - | - | 25.5 | 11.7 | 68.4 |
| **GPT-4-Turbo-1106** | - | - | 22.8 | 22.7 | 65.4 |
| **GPT-4-Turbo-0409** | - | - | 24.3 | 18.3 | 63.9 |
| **GPT-4o-0513** | - | - | **26.1** | **26.7** | **72.9** |
| **Open-Source Models** | | | | | |
| **CodeStral** | 22B | 22B | 17.8 | 2.7 | 51.1 |
| **DeepSeek-Coder-Instruct** | 33B | 33B | 11.3 | 0.0 | 54.5 |
| **Llama3-Instruct** | 70B | 70B | 16.2 | - | 49.2 |
| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 9.2 | 0.0 | 44.4 |
| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **21.0** | **12.7** | **73.7** |
### 3.4 Mathematical Reasoning
| | #TP | #AP | GSM8K | MATH | AIME 2024 | Math Odyssey |
| ----------------------------------- | :--: | :--: | :------: | :------: | :-------: | :----------: |
| **Closed-Source Models** | | | | | | |
| **Gemini-1.5-Pro** | - | - | 90.8 | 67.7 | 2/30 | 45.0 |
| **Claude-3-Opus** | - | - | 95.0 | 60.1 | 2/30 | 40.6 |
| **GPT-4-Turbo-1106** | - | - | 91.4 | 64.3 | 1/30 | 49.1 |
| **GPT-4-Turbo-0409** | - | - | 93.7 | 73.4 | **3/30** | 46.8 |
| **GPT-4o-0513** | - | - | **95.8** | **76.6** | 2/30 | **53.2** |
| **Open-Source Models** | | | | | | |
| **Llama3-Instruct** | 70B | 70B | 93.0 | 50.4 | 1/30 | 27.9 |
| **DeepSeek-Coder-V2-Lite-Instruct** | 16B | 2.4B | 86.4 | 61.8 | 0/30 | 44.4 |
| **DeepSeek-Coder-V2-Instruct** | 236B | 21B | **94.9** | **75.7** | **4/30** | **53.7** |
### 3.5 General Natural Language
| Benchmark | Domain | DeepSeek-V2-Lite Chat | DeepSeek-Coder-V2-Lite Instruct | DeepSeek-V2 Chat | DeepSeek-Coder-V2 Instruct |
| :------------------: | :-----: | :-------------------: | :-----------------------------: | :--------------: | :------------------------: |
| **BBH** | English | 48.1 | 61.2 | 79.7 | **83.9** |
| **MMLU** | English | 55.7 | 60.1 | 78.1 | **79.2** |
| **ARC-Easy** | English | 86.1 | 88.9 | **98.1** | 97.4 |
| **ARC-Challenge** | English | 73.4 | 77.4 | 92.3 | **92.8** |
| **TriviaQA** | English | 65.2 | 59.5 | **86.7** | 82.3 |
| **NaturalQuestions** | English | 35.5 | 30.8 | **53.4** | 47.5 |
| **AGIEval** | English | 42.8 | 28.7 | **61.4** | 60 |
| **CLUEWSC** | Chinese | 80.0 | 76.5 | **89.9** | 85.9 |
| **C-Eval** | Chinese | 60.1 | 61.6 | 78.0 | **79.4** |
| **CMMLU** | Chinese | 62.5 | 62.7 | **81.6** | 80.9 |
| **Arena-Hard** | - | 11.4 | 38.1 | 41.6 | **65.0** |
| **AlpaceEval 2.0** | - | 16.9 | 17.7 | **38.9** | 36.9 |
| **MT-Bench** | - | 7.37 | 7.81 | **8.97** | 8.77 |
| **Alignbench** | - | 6.02 | 6.83 | **7.91** | 7.84 |
### 3.6 Context Window
<p align="center">
<img width="80%" src="figures/long_context.png">
</p>
Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-Coder-V2 performs well across all context window lengths up to **128K**.
## 4. Chat Website
You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
## 5. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
<p align="center">
<img width="40%" src="figures/model_price.jpg">
</p>
## 6. How to run locally
**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
### Inference with Huggingface's Transformers
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
#### Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### Chat Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
An example of chat template is as belows:
```bash
<｜begin▁of▁sentence｜>User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
You can also add an optional system message:
```bash
<｜begin▁of▁sentence｜>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
### Inference with vLLM (recommended)
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
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": "write a quick sort algorithm in python."}],
[{"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)
```
## 7. License
This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
## 8. Citation
```latex
@article{zhu2024deepseek,
title={DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence},
author={Zhu, Qihao and Guo, Daya and Shao, Zhihong and Yang, Dejian and Wang, Peiyi and Xu, Runxin and Wu, Y and Li, Yukun and Gao, Huazuo and Ma, Shirong and others},
journal={arXiv preprint arXiv:2406.11931},
year={2024}
}
```
## 9. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
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