Instructions to use deepseek-ai/deepseek-moe-16b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/deepseek-moe-16b-chat with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/deepseek-moe-16b-chat", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-moe-16b-chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/deepseek-moe-16b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/deepseek-moe-16b-chat"
# 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-moe-16b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/deepseek-moe-16b-chat

SGLang

How to use deepseek-ai/deepseek-moe-16b-chat 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-moe-16b-chat" \
    --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-moe-16b-chat",
		"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-moe-16b-chat" \
        --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-moe-16b-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/deepseek-moe-16b-chat with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/deepseek-moe-16b-chat
```

deepseek-moe-16b-chat / README.md

luofuli

Update README.md

d469c91 verified over 2 years ago

preview code

Raw

History Blame

2.84 kB

	---
	license: other
	license_name: deepseek
	license_link: https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL
	---


	<p align="center">
	<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
	</p>
	<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> \| <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> \| <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> \| <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>

	<p align="center">
	<a href="https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/DeepSeekMoE.pdf"><b>Paper Preview</b>👁️</a>
	</p>

	<hr>





	### 1. Introduction to DeepSeekMoE
	See the [Introduction](https://github.com/deepseek-ai/DeepSeek-MoE/blob/main) for more details.

	### 2. How to Use
	Here give some examples of how to use our model.

	Chat Completion

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

	model_name = "deepseek-ai/deepseek-moe-16b-chat"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
	model.generation_config = GenerationConfig.from_pretrained(model_name)
	model.generation_config.pad_token_id = model.generation_config.eos_token_id

	messages = [
	{"role": "user", "content": "Who are you?"}
	]
	input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
	outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)

	result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
	print(result)
	```

	Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.

	```
	User: {messages[0]['content']}

	Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}

	Assistant:
	```

	Note: By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.


	### 3. License
	This code repository is licensed under the MIT License. The use of DeepSeekMoE models is subject to the Model License. DeepSeekMoE supports commercial use.

	See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-MoE/blob/main/LICENSE-MODEL) for more details.

	### 4. Contact

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