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https://github.com/deepseek-ai/DeepSeek-Coder-V2
https://github.com/deepseek-ai/DeepSeek-Coder-V2
Last synced: 9 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/deepseek-ai/DeepSeek-Coder-V2
- Owner: deepseek-ai
- License: mit
- Created: 2024-06-14T03:39:37.000Z (14 days ago)
- Default Branch: main
- Last Pushed: 2024-06-18T03:46:29.000Z (10 days ago)
- Last Synced: 2024-06-18T13:55:15.771Z (9 days ago)
- Size: 1.98 MB
- Stars: 312
- Watchers: 8
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE-CODE
Lists
- Awesome-LLM - DeepSeek-Coder-v2-16|236B-MOE
- Awesome-LLM?tab=readme-ov-file - DeepSeek-Coder-v2-16|236B-MOE
- awesome-list - deepseek-ai/DeepSeek-Coder-V2 - DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (Others)
- awesome - deepseek-ai/DeepSeek-Coder-V2 - DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (miscellaneous)
README
Model Download |
Evaluation Results |
API Platform |
How to Use |
License |
Citation
# 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.
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.
| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [š¤ HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [š¤ HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [š¤ HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [š¤ HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
## 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
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/). Sign up for over millions of free tokens. And you can also pay-as-you-go at an unbeatable price.
## 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 = """<ļ½fimābeginļ½>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<ļ½fimāholeļ½>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<ļ½fimāendļ½>"""
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
<ļ½begināofāsentenceļ½>User: {user_message_1}
Assistant: {assistant_message_1}<ļ½endāofāsentenceļ½>User: {user_message_2}
Assistant:
```
You can also add an optional system message:
```bash
<ļ½begināofāsentenceļ½>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<ļ½endāofāsentenceļ½>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{deepseek-coder-v2,
author={Qihao Zhu and Daya Guo and Zhihong Shao and Dejian Yang and Peiyi Wang and Runxin Xu and Y. Wu and Yukun Li and Huazuo Gao and Shirong Ma and Wangding Zeng and Xiao Bi and Zihui Gu and Hanwei Xu and Damai Dai and Kai Dong and Liyue Zhang and Yishi Piao and Zhibin Gou and Zhenda Xie and Zhewen Hao and Bingxuan Wang and Junxiao Song and Deli Chen and Xin Xie and Kang Guan and Yuxiang You and Aixin Liu and Qiushi Du and Wenjun Gao and Xuan Lu and Qinyu Chen and Yaohui Wang and Chengqi Deng and Jiashi Li and Chenggang Zhao and Chong Ruan and Fuli Luo and Wenfeng Liang},
title={DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence},
year={2024},
url={https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf}
}
```
## 9. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
