Instructions to use Fileportz/DeepSeek-V4.1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fileportz/DeepSeek-V4.1-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Fileportz/DeepSeek-V4.1-Flash")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Fileportz/DeepSeek-V4.1-Flash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Fileportz/DeepSeek-V4.1-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Fileportz/DeepSeek-V4.1-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Fileportz/DeepSeek-V4.1-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Fileportz/DeepSeek-V4.1-Flash
- SGLang
How to use Fileportz/DeepSeek-V4.1-Flash 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 "Fileportz/DeepSeek-V4.1-Flash" \ --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": "Fileportz/DeepSeek-V4.1-Flash", "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 "Fileportz/DeepSeek-V4.1-Flash" \ --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": "Fileportz/DeepSeek-V4.1-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Fileportz/DeepSeek-V4.1-Flash with Docker Model Runner:
docker model run hf.co/Fileportz/DeepSeek-V4.1-Flash
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{
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "请按“第一张、第二张”的顺序回答:第一张图"
},
{
"type": "image_url",
"image_url": {
"url": "examples/images/carrots.jpeg"
}
},
{
"type": "text",
"text": "和第二张图"
},
{
"type": "image_url",
"image_url": {
"url": "examples/images/corn.jpeg"
}
},
{
"type": "text",
"text": "中分别是什么食材?它们通常食用的部位分别是什么?"
}
]
}
]
},
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "中国的首都是哪里?"
}
]
},
{
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the weather for a specific location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "The city name"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}
],
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What's the weather like in Beijing?"
}
]
},
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hi there! How can I help you?"
},
{
"role": "system",
"content": "Mid-conversation instruction update: reply in Chinese only. (deepseek_v41 only)"
}
]
}
]
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