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https://github.com/deepseek-ai/DeepSeek-Coder-V2
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
https://github.com/deepseek-ai/DeepSeek-Coder-V2
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DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
- Host: GitHub
- URL: https://github.com/deepseek-ai/DeepSeek-Coder-V2
- Owner: deepseek-ai
- License: mit
- Created: 2024-06-14T03:39:37.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-09-24T12:09:45.000Z (3 months ago)
- Last Synced: 2024-10-15T23:46:13.567Z (about 2 months ago)
- Homepage:
- Size: 2.01 MB
- Stars: 2,061
- Watchers: 22
- Forks: 104
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE-CODE
- Support: supported_langs.txt
Awesome Lists containing this project
- awesome-generative-ai-guide - DeepSeek-Coder-V2 - 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. | Domain Specific LLMs | (:star: Best Gen AI Papers List (June 2024))
- awesome - deepseek-ai/DeepSeek-Coder-V2 - DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (miscellaneous)
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- awesome-ai-models - DeepSeek-Coder-v2-16|236B-MOE - Released on December 2023 (Uncategorized / Uncategorized)
- StarryDivineSky - deepseek-ai/DeepSeek-Coder-V2 - Turbo 相当的性能。具体来说，DeepSeek-Coder-V2 从 DeepSeek-V2 的中间检查点进一步预训练，并增加了 6 万亿个令牌。通过这种持续的预训练，DeepSeek-Coder-V2 大大增强了 DeepSeek-V2 的编码和数学推理能力，同时在一般语言任务中保持了相当的性能。与 DeepSeek-Coder-33B 相比，DeepSeek-Coder-V2 在与代码相关的任务的各个方面以及推理和通用功能方面都取得了重大进步。此外，DeepSeek-Coder-V2 将其对编程语言的支持从 86 扩展到 338，同时将上下文长度从 16K 扩展到 128K。 (A01_文本生成_文本对话 / 大语言对话模型及数据)
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 | [🤗 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) |
## 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/), 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 = """<｜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 <｜end▁of▁sentence｜> 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:
```
In the last round of dialogue, note that "Assistant:" has no space after the colon. Adding a space might cause the following issues on the 16B-Lite model:
- English questions receiving Chinese responses.
- Responses containing garbled text.
- Responses repeating excessively.
Older versions of Ollama had this bug (see https://github.com/deepseek-ai/DeepSeek-Coder-V2/issues/12), but it has been fixed in the latest version.
### Inference with SGLang (recommended)
[SGLang](https://github.com/sgl-project/sglang) currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, offering the best latency and throughput among open-source frameworks. Here are some example commands to launch an OpenAI API-compatible server:
```bash
# BF16, tensor parallelism = 8
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Instruct --tp 8 --trust-remote-code
# BF16, w/ torch.compile (The compilation can take several minutes)
python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --trust-remote-code --enable-torch-compile
# FP8, tensor parallelism = 8, FP8 KV cache
python3 -m sglang.launch_server --model neuralmagic/DeepSeek-Coder-V2-Instruct-FP8 --tp 8 --trust-remote-code --kv-cache-dtype fp8_e5m2
```
After launching the server, you can query it with OpenAI API
```
import openai
client = openai.Client(
base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
# Chat completion
response = client.chat.completions.create(
model="default",
messages=[
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print(response)
```
### 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 [[email protected]]([email protected]).
