Instructions to use VECTORVV1/vector-V4-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VECTORVV1/vector-V4-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VECTORVV1/vector-V4-Pro")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("VECTORVV1/vector-V4-Pro") model = AutoModelForCausalLM.from_pretrained("VECTORVV1/vector-V4-Pro") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use VECTORVV1/vector-V4-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VECTORVV1/vector-V4-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VECTORVV1/vector-V4-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VECTORVV1/vector-V4-Pro
- SGLang
How to use VECTORVV1/vector-V4-Pro 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 "VECTORVV1/vector-V4-Pro" \ --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": "VECTORVV1/vector-V4-Pro", "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 "VECTORVV1/vector-V4-Pro" \ --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": "VECTORVV1/vector-V4-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VECTORVV1/vector-V4-Pro with Docker Model Runner:
docker model run hf.co/VECTORVV1/vector-V4-Pro
| <|begin▁of▁sentence|>You are a helpful assistant. | |
| ## Tools | |
| You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<|DSML|tool_calls>" block like the following: | |
| <|DSML|tool_calls> | |
| <|DSML|invoke name="$TOOL_NAME"> | |
| <|DSML|parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</|DSML|parameter> | |
| ... | |
| </|DSML|invoke> | |
| <|DSML|invoke name="$TOOL_NAME2"> | |
| ... | |
| </|DSML|invoke> | |
| </|DSML|tool_calls> | |
| String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`. | |
| If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response. | |
| Otherwise, output directly after </think> with tool calls or final response. | |
| ### Available Tool Schemas | |
| {"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"], "description": "Temperature unit"}}, "required": ["location"]}} | |
| {"name": "search", "description": "Search the web for information", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "integer", "description": "Number of results to return"}}, "required": ["query"]}} | |
| You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. | |
| <|User|>What's the weather in Beijing?<|Assistant|><think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think> | |
| <|DSML|tool_calls> | |
| <|DSML|invoke name="get_weather"> | |
| <|DSML|parameter name="location" string="true">Beijing</|DSML|parameter> | |
| <|DSML|parameter name="unit" string="true">celsius</|DSML|parameter> | |
| </|DSML|invoke> | |
| </|DSML|tool_calls><|end▁of▁sentence|><|User|><tool_result>{"temperature": 22, "condition": "sunny", "humidity": 45}</tool_result><|Assistant|><think>Got the weather data. Let me format a nice response.</think>The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.<|end▁of▁sentence|> |