├── LICENSE-CODE
├── LICENSE-MODEL
├── README.md
├── figures
├── badge.svg
├── logo.svg
├── long_context.png
├── model_price.jpg
├── performance.png
└── qr.jpeg
├── paper.pdf
└── supported_langs.txt
/LICENSE-CODE:
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1 | DEEPSEEK LICENSE AGREEMENT
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3 | Version 1.0, 23 October 2023
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5 | Copyright (c) 2023 DeepSeek
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7 | Section I: PREAMBLE
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72 |
73 | END OF TERMS AND CONDITIONS
74 |
75 | Attachment A
76 |
77 | Use Restrictions
78 |
79 | You agree not to use the Model or Derivatives of the Model:
80 |
81 | - In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
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90 | - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
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/README.md:
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1 |
2 |
3 |
4 |
5 |
54 |
55 |
56 | # DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
57 |
58 | ## 1. Introduction
59 | 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.
60 |
61 |
62 |
63 |
64 |
65 |
66 | 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).
67 |
68 | ## 2. Model Downloads
69 |
70 | 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.
71 |
72 |
172 |
173 |
174 | Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-Coder-V2 performs well across all context window lengths up to **128K**.
175 |
176 | ## 4. Chat Website
177 |
178 | You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
179 |
180 | ## 5. API Platform
181 | 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.
182 |
183 |
184 |
185 |
186 |
187 |
188 | ## 6. How to run locally
189 | **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.**
190 |
191 | ### Inference with Huggingface's Transformers
192 | You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
193 |
194 | #### Code Completion
195 | ```python
196 | from transformers import AutoTokenizer, AutoModelForCausalLM
197 | import torch
198 | tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
199 | model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
200 | input_text = "#write a quick sort algorithm"
201 | inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
202 | outputs = model.generate(**inputs, max_length=128)
203 | print(tokenizer.decode(outputs[0], skip_special_tokens=True))
204 | ```
205 |
206 | #### Code Insertion
207 | ```python
208 | from transformers import AutoTokenizer, AutoModelForCausalLM
209 | import torch
210 | tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
211 | model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
212 | input_text = """<｜fim▁begin｜>def quick_sort(arr):
213 | if len(arr) <= 1:
214 | return arr
215 | pivot = arr[0]
216 | left = []
217 | right = []
218 | <｜fim▁hole｜>
219 | if arr[i] < pivot:
220 | left.append(arr[i])
221 | else:
222 | right.append(arr[i])
223 | return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
224 | inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
225 | outputs = model.generate(**inputs, max_length=128)
226 | print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
227 | ```
228 |
229 | #### Chat Completion
230 |
231 | ```python
232 | from transformers import AutoTokenizer, AutoModelForCausalLM
233 | import torch
234 | tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
235 | model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
236 | messages=[
237 | { 'role': 'user', 'content': "write a quick sort algorithm in python."}
238 | ]
239 | inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
240 | # tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜> token
241 | 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)
242 | print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
243 | ```
244 |
245 |
246 |
247 | The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
248 |
249 | An example of chat template is as belows:
250 |
251 | ```bash
252 | <｜begin▁of▁sentence｜>User: {user_message_1}
253 |
254 | Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
255 |
256 | Assistant:
257 | ```
258 |
259 | You can also add an optional system message:
260 |
261 | ```bash
262 | <｜begin▁of▁sentence｜>{system_message}
263 |
264 | User: {user_message_1}
265 |
266 | Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
267 |
268 | Assistant:
269 | ```
270 |
271 | 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:
272 | - English questions receiving Chinese responses.
273 | - Responses containing garbled text.
274 | - Responses repeating excessively.
275 |
276 | 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.
277 |
278 |
279 | ### Inference with SGLang (recommended)
280 | [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:
281 |
282 | ```bash
283 | # BF16, tensor parallelism = 8
284 | python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Instruct --tp 8 --trust-remote-code
285 |
286 | # BF16, w/ torch.compile (The compilation can take several minutes)
287 | python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct --trust-remote-code --enable-torch-compile
288 |
289 | # FP8, tensor parallelism = 8, FP8 KV cache
290 | python3 -m sglang.launch_server --model neuralmagic/DeepSeek-Coder-V2-Instruct-FP8 --tp 8 --trust-remote-code --kv-cache-dtype fp8_e5m2
291 | ```
292 |
293 | After launching the server, you can query it with OpenAI API
294 |
295 | ```
296 | import openai
297 | client = openai.Client(
298 | base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
299 |
300 | # Chat completion
301 | response = client.chat.completions.create(
302 | model="default",
303 | messages=[
304 | {"role": "system", "content": "You are a helpful AI assistant"},
305 | {"role": "user", "content": "List 3 countries and their capitals."},
306 | ],
307 | temperature=0,
308 | max_tokens=64,
309 | )
310 | print(response)
311 | ```
312 |
313 |
314 | ### Inference with vLLM (recommended)
315 | 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.
316 |
317 | ```python
318 | from transformers import AutoTokenizer
319 | from vllm import LLM, SamplingParams
320 |
321 | max_model_len, tp_size = 8192, 1
322 | model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
323 | tokenizer = AutoTokenizer.from_pretrained(model_name)
324 | llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
325 | sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
326 |
327 | messages_list = [
328 | [{"role": "user", "content": "Who are you?"}],
329 | [{"role": "user", "content": "write a quick sort algorithm in python."}],
330 | [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
331 | ]
332 |
333 | prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
334 |
335 | outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
336 |
337 | generated_text = [output.outputs[0].text for output in outputs]
338 | print(generated_text)
339 | ```
340 |
341 |
342 |
343 | ## 7. License
344 |
345 | 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.
346 |
347 | ## 8. Citation
348 | ```latex
349 | @article{zhu2024deepseek,
350 | title={DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence},
351 | 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},
352 | journal={arXiv preprint arXiv:2406.11931},
353 | year={2024}
354 | }
355 | ```
356 |
357 | ## 9. Contact
358 | If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
359 |
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/supported_langs.txt:
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1 | ABAP
2 | AGS Script
3 | AMD GPU
4 | AMPL
5 | ANSYS Parametric Design Language
6 | ANTLR
7 | APL
8 | ASP
9 | AWK
10 | ActionScript
11 | Ada
12 | Agda
13 | Alloy
14 | AmbientTalk
15 | Apache Configuration
16 | AppleScript
17 | Arc
18 | Arduino
19 | AspectJ
20 | Assembly
21 | Asymptote
22 | Augeas
23 | AutoHotkey
24 | AutoIt
25 | BC
26 | BNF
27 | BST
28 | Berry
29 | BitBake
30 | BlitzBasic
31 | BlitzMax
32 | Bluespec
33 | Boo
34 | Boogie
35 | Brainfuck
36 | BrightScript
37 | Bro
38 | C
39 | C#
40 | C++
41 | C2HS Haskell
42 | CADL
43 | CMake
44 | COBOL
45 | COBOLFree
46 | CSS
47 | CUDA
48 | CapDL
49 | Ceylon
50 | Chapel
51 | ChucK
52 | Cirru
53 | Click
54 | Clojure
55 | CoffeeScript
56 | ColdFusion CFC
57 | Common Lisp
58 | Crystal
59 | Csound
60 | Csound Score
61 | Cypher
62 | Cython
63 | DASM16
64 | DM
65 | Darcs Patch
66 | Dart
67 | Debian Control File
68 | DeviceTree
69 | Diff
70 | Docker
71 | Dockerfile
72 | Dylan
73 | EBNF
74 | ELPi
75 | Eiffel
76 | Elixir
77 | Elm
78 | Emacs Lisp
79 | EmberScript
80 | Erlang
81 | Execline
82 | F#
83 | F*
84 | Factor
85 | Fancy
86 | Fantom
87 | Felix
88 | Fennel
89 | Fish
90 | Flux
91 | Fortran
92 | Fortran Fixed Form
93 | FoxPro
94 | FreeFem
95 | FreeMarker
96 | Futhark
97 | G-Code
98 | GAP
99 | GAS
100 | GDScript
101 | GLSL
102 | GSQL
103 | Genshi
104 | Gentoo Ebuild
105 | Gentoo Eclass
106 | Gettext Catalog
107 | Glyph
108 | Gnuplot
109 | Go
110 | Gosu
111 | Grace
112 | Gradle
113 | Grammatical Framework
114 | GraphQL
115 | Graphviz DOT
116 | Groff
117 | Groovy
118 | Groovy Server Pages
119 | HCL
120 | HLSL
121 | HTML
122 | HTML Django
123 | HTML ERB
124 | HTML PHP
125 | HTTP
126 | Handlebars
127 | Haskell
128 | Haxe
129 | Hy
130 | IGOR Pro
131 | Idris
132 | Inform 6 Template
133 | Inno Setup
134 | Io
135 | Isabelle
136 | J
137 | JAGS
138 | JCL
139 | JFlex
140 | JSON
141 | JSONiq
142 | JSX
143 | Jade
144 | Jasmin
145 | Java
146 | Java Server Pages
147 | JavaScript
148 | JavaScript MozPreproc
149 | Julia
150 | Jupyter Notebook
151 | K
152 | KRL
153 | Kconfig
154 | Koka
155 | Kotlin
156 | LFE
157 | LLVM
158 | LSL
159 | Lean
160 | Less
161 | Lex
162 | Lighttpd Configuration File
163 | LilyPond
164 | Limbo
165 | Linker Script
166 | Liquid
167 | Literate Agda
168 | Literate CoffeeScript
169 | Logtalk
170 | Lua
171 | M4
172 | MATLAB
173 | MQL
174 | MUF
175 | Makefile
176 | Mako
177 | Mason
178 | Maxima
179 | Meson
180 | Metal
181 | MiniScript
182 | Mirah
183 | Mizar
184 | Modelica
185 | Modula-2
186 | Monkey
187 | MooCode
188 | MoonScript
189 | Mosel
190 | MuPAD
191 | NASM
192 | NCL
193 | NSIS
194 | NetLinx
195 | Nginx Configuration File
196 | Nimrod
197 | Ninja
198 | Nit
199 | Nix
200 | Nu
201 | NuSMV
202 | OCaml
203 | OMG Interface Definition Language
204 | Objdump
205 | Objective-C
206 | Objective-C++
207 | Octave
208 | Odin
209 | Opa
210 | OpenCL
211 | OpenEdge ABL
212 | OpenSCAD
213 | Ox
214 | Oz
215 | PAWN
216 | PEG
217 | PHP
218 | POD
219 | POV-Ray
220 | Papyrus
221 | Parrot Internal Representation
222 | Pascal
223 | Perl
224 | Perl 6
225 | Pike
226 | PkgConfig
227 | Pony
228 | PowerShell
229 | Praat
230 | Processing
231 | Propeller Spin
232 | Protocol Buffer
233 | Pug
234 | Puppet
235 | PureBasic
236 | PureScript
237 | Python
238 | Q
239 | QML
240 | QVTO
241 | R
242 | RAML
243 | RConsole
244 | REALbasic
245 | REXX
246 | RHTML
247 | Racket
248 | Ragel in Ruby Host
249 | Rd
250 | ReasonML
251 | Red
252 | Ren'Py
253 | RenderScript
254 | Ride
255 | Robot Framework
256 | Rouge
257 | Ruby
258 | Rust
259 | S
260 | SARL
261 | SAS
262 | SCSS
263 | SMT
264 | SPARQL
265 | SQF
266 | SQL
267 | SWIG
268 | Sage
269 | Sass
270 | Scala
271 | Scheme
272 | Scilab
273 | Self
274 | ShExC
275 | Shell
276 | Sieve
277 | Silver
278 | Singularity
279 | Slim
280 | Smali
281 | Smarty
282 | Smithy
283 | Solidity
284 | SourcePawn
285 | Squirrel
286 | Stan
287 | Standard ML
288 | Stata
289 | Stylus
290 | SuperCollider
291 | Swift
292 | SystemVerilog
293 | Tcl
294 | Tcsh
295 | TeX
296 | Tea
297 | Terminfo
298 | Thrift
299 | Transact-SQL
300 | Treetop
301 | Turing
302 | Twig
303 | TypeScript
304 | TypoScript
305 | USD
306 | Unity3D Asset
307 | Uno
308 | UnrealScript
309 | UrWeb
310 | VBScript
311 | VCL
312 | VHDL
313 | Vala
314 | Velocity
315 | Verilog
316 | VimL
317 | Visual Basic
318 | Vue
319 | Web IDL
320 | WebAssembly
321 | Whiley
322 | X10
323 | XBase
324 | XC
325 | XML
326 | XML Lasso
327 | XQuery
328 | XS
329 | XSLT
330 | Xtend
331 | Xtlang
332 | YANG
333 | Zeek
334 | Zephir
335 | Zig
336 | Zimpl
337 | eC
338 | ooc
339 |
--------------------------------------------------------------------------------
7 | 
63 | 
171 | 
185 | 
12 | 
15 | 
18 | 
24 | 
27 | 
30 | 
35 | 
38 |