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

Libraries

How to use deepseek-ai/DeepSeek-V2-Chat with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V2-Chat", 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-V2-Chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V2-Chat", 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-V2-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-V2-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-V2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-V2-Chat

SGLang

How to use deepseek-ai/DeepSeek-V2-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-V2-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-V2-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-V2-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-V2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

fail to run the example

by Leymore - opened May 7, 2024

Discussion

Leymore

May 7, 2024

•

edited May 7, 2024

first, model_path is not defined ,

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory)

should be

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory)

then this error comes up:

  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 558, in from_pretrained
    return model_class.from_pretrained(
  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3550, in from_pretrained
    model = cls(config, *model_args, **model_kwargs)
  File "/root/.cache/huggingface/modules/transformers_modules/deepseek-ai/DeepSeek-V2-Chat/2c190bb36705793d723008b0c1fa3a691c227485/modeling_deepseek.py", line 1588, in __init__
    self.model = DeepseekV2Model(config)
  File "/root/.cache/huggingface/modules/transformers_modules/deepseek-ai/DeepSeek-V2-Chat/2c190bb36705793d723008b0c1fa3a691c227485/modeling_deepseek.py", line 1404, in __init__
    [
  File "/root/.cache/huggingface/modules/transformers_modules/deepseek-ai/DeepSeek-V2-Chat/2c190bb36705793d723008b0c1fa3a691c227485/modeling_deepseek.py", line 1405, in <listcomp>
    DeepseekV2DecoderLayer(config, layer_idx)
  File "/root/.cache/huggingface/modules/transformers_modules/deepseek-ai/DeepSeek-V2-Chat/2c190bb36705793d723008b0c1fa3a691c227485/modeling_deepseek.py", line 1187, in __init__
    self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
KeyError: 'sdpa'

added attn_implementation argument:

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", attn_implementation='flash_attention_2', torch_dtype=torch.bfloat16, max_memory=max_memory)

it will take about 0.5 hour to load the model, and this error comes up:

  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 558, in from_pretrained
    return model_class.from_pretrained(
  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3735, in from_pretrained
    dispatch_model(model, **device_map_kwargs)
  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/accelerate/big_modeling.py", line 364, in dispatch_model
    weights_map = OffloadedWeightsLoader(
  File "/cpfs01/user/zhoufengzhe/anaconda3/envs/lmdeploy/lib/python3.10/site-packages/accelerate/utils/offload.py", line 150, in __init__
    raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights.")

i am not sure the offload scheme should be activated.

anyway, has anyone run the model successfully?

Leymore

May 7, 2024

btw, i am using 8 * 80G A100

msr2000

DeepSeek org May 7, 2024

Thanks for pointing out the typo and it's fixed now. Regarding sdpa attention implementation in HuggingFace, we've had to remove it due to some issues. You might want to try our default eager implementation instead.

Moreover, the HuggingFace's code is not as efficient as we would like, so we're developing a new open-source code using vLLM for better performance.

Debang

May 9, 2024

•

edited May 9, 2024

when I can change the attn_implementation to eager with

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation='eager')

still has the error

ValueError: Need either a state_dict or a save_folder containing offloaded weights

@msr2000

ElissaHu

May 9, 2024

I had meet the same problem when loading deekseek-v2:

File "/usr/local/lib/python3.8/dist-packages/accelerate/utils/offload.py", line 150, in init
raise ValueError("Need either a state_dict or a save_folder containing offloaded weights.")
ValueError: Need either a state_dict or a save_folder containing offloaded weights.`

msr2000

DeepSeek org May 9, 2024

The HuggingFace code and examples have been updated recently. Note that the line of code for loading model should be replaced with:

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager")

Please try again.

ElissaHu

May 9, 2024

I had try this：
replace

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory)

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory, offload_folder="save_folder")

and it's work.

msr2000

DeepSeek org May 9, 2024

I had try this：
replace

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory)

model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16, max_memory=max_memory, offload_folder="save_folder")

and it's work.

Regarding the issue with accelerate library in GPU memory computation, it's important to note that offloading is actually unnecessary when there is ample GPU memory available (80GB * 8). Please refer to my previous response for more details.

Leymore

May 9, 2024

•

edited May 9, 2024

with

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager")

i can run successfully, cheers~

note device_map="sequential"

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