deepseek-ai/DeepSeek-Coder

deepseek-ai/DeepSeek-Coder

Description: DeepSeek Coder: Let the Code Write Itself

Language: Python

License: MIT

Stars: 23659

Forks: 2845

Open issues: 163

Created: 2023-10-20T06:38:01Z

Pushed: 2025-11-11T06:44:56Z

Default branch: main

Fork: no

Archived: no

README:

[ Homepage] | [🤖 Chat with DeepSeek Coder] | [🤗 Models Download] | [Discord] | [WeChat (微信)]

Paper Link👁️

1. Introduction of DeepSeek Coder

DeepSeek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and an extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, DeepSeek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.

• Massive Training Data: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.

• Highly Flexible & Scalable: Offered in model sizes of 1B, 5.7B, 6.7B and 33B, enabling users to choose the setup most suitable for their requirements.

• Superior Model Performance: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.

• Advanced Code Completion Capabilities: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.

Supported Programming Languages

['ada', 'agda', 'alloy', 'antlr', 'applescript', 'assembly', 'augeas', 'awk', 'batchfile', 'bluespec', 'c', 'c-sharp', 'clojure', 'cmake', 'coffeescript', 'common-lisp', 'cpp', 'css', 'cuda', 'dart', 'dockerfile', 'elixir', 'elm', 'emacs-lisp', 'erlang', 'f-sharp', 'fortran', 'glsl', 'go', 'groovy', 'haskell', 'html', 'idris', 'isabelle', 'java', 'java-server-pages', 'javascript', 'json', 'julia', 'jupyter-notebook', 'kotlin', 'lean', 'literate-agda', 'literate-coffeescript', 'literate-haskell', 'lua', 'makefile', 'maple', 'markdown', 'mathematica', 'matlab', 'ocaml', 'pascal', 'perl', 'php', 'powershell', 'prolog', 'protocol-buffer', 'python', 'r', 'racket', 'restructuredtext', 'rmarkdown', 'ruby', 'rust', 'sas', 'scala', 'scheme', 'shell', 'smalltalk', 'solidity', 'sparql', 'sql', 'stan', 'standard-ml', 'stata', 'systemverilog', 'tcl', 'tcsh', 'tex', 'thrift', 'typescript', 'verilog', 'vhdl', 'visual-basic', 'xslt', 'yacc', 'yaml', 'zig']

2. Evaluation Results

We evaluate DeepSeek Coder on various coding-related benchmarks. Only pass@1 results on HumanEval (Python and Multilingual), MBPP, and DS-1000 are reported here:

The result shows that DeepSeek-Coder-Base-33B significantly outperforms existing open-source code LLMs. Compared with CodeLlama-34B, it leads by 7.9%, 9.3%, 10.8% and 5.9% respectively on HumanEval Python, HumanEval Multilingual, MBPP and DS-1000. Surprisingly, our DeepSeek-Coder-Base-7B reaches the performance of CodeLlama-34B. The DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms GPT35-turbo on HumanEval and achieves comparable results with GPT35-turbo on MBPP.

More evaluation details can be found in the [Detailed Evaluation](#6-detailed-evaluation-results).

3. Procedure of Data Creation and Model Training

Data Creation

• Step 1: Collect code data from GitHub and apply the same filtering rules as StarCoder Data to filter data.
• Step 2: Parsing the dependencies of files within the same repository to rearrange the file positions based on their dependencies.
• Step 3: Concatenating dependent files to form a single example and employ repo-level minhash for deduplication.
• Step 4: Further filtering out low-quality code, such as codes with syntax errors or poor readability.

Model Training

• Step 1: Initially pre-trained with a dataset consisting of 87% code, 10% code-related language (Github Markdown and StackExchange), and 3% non-code-related Chinese language. Models are pre-trained using 1.8T tokens and a 4K window size in this step.
• Step 2: Further Pre-training using an extended 16K window size on an additional 200B tokens, resulting in foundational models (DeepSeek-Coder-Base).
• Step 3: Instruction Fine-tuning on 2B tokens of instruction data, resulting in instruction-tuned models (DeepSeek-Coder-Instruct).

4. How to Use

Before proceeding, you'll need to install the necessary dependencies. You can do this by running the following command:

pip install -r requirements.txt

A demo is also available on the 🤗 Hugging Face Space, and you can run the demo locally using app.py in the demo folder. (Thanks to all the HF team for their support)

Here are some examples of how to use our model.

1) Code Completion

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-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))

This code will output the following result:

def quick_sort(arr):
if len(arr) def quick_sort(arr):
if len(arr)
if arr[i] """
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):])

This code will output the following result:

for i in range(1, len(arr)):

3) Chat Model Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct", trust_remote_code=True,…