Instructions to use deepseek-ai/deepseek-vl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/deepseek-vl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="deepseek-ai/deepseek-vl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deepseek-ai/deepseek-vl2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use deepseek-ai/deepseek-vl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/deepseek-vl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-vl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/deepseek-ai/deepseek-vl2
- SGLang
How to use deepseek-ai/deepseek-vl2 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-vl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-vl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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-vl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/deepseek-vl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use deepseek-ai/deepseek-vl2 with Docker Model Runner:
docker model run hf.co/deepseek-ai/deepseek-vl2
Run on vLLM
Got the error below:
ValueError: The checkpoint you are trying to load has model type deepseek_vl_v2 but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.
Got the error below:
ValueError: The checkpoint you are trying to load has model typedeepseek_vl_v2but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.
same error when deploying using vllm. Did you manage to solve? Thanks
Got the error below:
ValueError: The checkpoint you are trying to load has model typedeepseek_vl_v2but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.same error when deploying using vllm. Did you manage to solve? Thanks
Hi, Have you solved the problem, Thanks
Got the error below:
ValueError: The checkpoint you are trying to load has model typedeepseek_vl_v2but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.same error when deploying using vllm. Did you manage to solve? Thanks
Hi, Have you solved the problem, Thanks
same
I also got the same error
Transformers supported model list does not contain DeepSeek. You will have to import the model provided by DeepSeek source code
import torch
from transformers import AutoModelForCausalLM
from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM
# specify the path to the model
model_path = "deepseek-ai/deepseek-vl2-small"
vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
After upgrade vllm to the latest version e.g. v0.7.2, and run vllm with option --hf_overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}', then everything is OK.