Instructions to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

SGLang

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
```

DeepSeek-Coder-V2-Lite-Instruct / README.md

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	---
	license: other
	license_name: deepseek-license
	license_link: LICENSE
	---
	<!-- markdownlint-disable first-line-h1 -->
	<!-- markdownlint-disable html -->
	<!-- markdownlint-disable no-duplicate-header -->

	<div align="center">
	<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
	</div>
	<hr>
	<div align="center" style="line-height: 1;">
	<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
	<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
	<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
	<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>

	<div align="center" style="line-height: 1;">
	<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
	<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
	<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
	<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>

	<div align="center" style="line-height: 1;">
	<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
	<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
	<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>
	<p align="center">
	<a href="#4-api-platform">API Platform</a> \|
	<a href="#5-how-to-run-locally">How to Use</a> \|
	<a href="#6-license">License</a> \|
	</p>


	<p align="center">
	<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><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="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true">
	</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](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/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. Chat Website

	You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)

	## 4. 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="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true">
	</p>


	## 5. 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, 80GB8 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:
	```

	### 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)
	```



	## 6. License

	This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.


	## 7. Contact
	If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).