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|>该助手为DeepSeek,由深度求索公司创造。<|latest_reminder|>2026-02-21,星期六,广州,App,中文<|User|>小柴胡冲剂和布洛芬能一起吃吗? | |
| CITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】 | |
| ## 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": "search", "description": "Web search. Split multiple queries with '||'.", "parameters": {"type": "object", "properties": {"queries": {"type": "string", "description": "query1||query2"}}, "required": ["queries"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| {"name": "open", "description": "Batch open IDs (format 【{id}†...】) or URLs.", "parameters": {"type": "object", "properties": {"open_list": {"type": "array", "items": {"type": "object", "properties": {"id": {"description": "ID or URL", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "", "default": -1}, "loc": {"type": "integer", "description": "Start line", "default": -1}, "num_lines": {"type": "integer", "description": "", "default": -1}, "view_source": {"type": "boolean", "description": "", "default": false}}, "additionalProperties": false}, "description": ""}}, "required": ["open_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| {"name": "find", "description": "Find exact text pattern in pages.", "parameters": {"type": "object", "properties": {"find_list": {"type": "array", "items": {"type": "object", "properties": {"pattern": {"type": "string", "description": ""}, "cursor": {"type": "integer", "description": "", "default": -1}}, "required": ["pattern"], "additionalProperties": false}, "description": ""}}, "required": ["find_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}} | |
| You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. | |
| <|Assistant|><think>用户想知道小柴胡冲剂和布洛芬能否一起服用。</think> | |
| <|DSML|tool_calls> | |
| <|DSML|invoke name="search"> | |
| <|DSML|parameter name="queries" string="true">小柴胡冲剂 布洛芬 相互作用 一起吃</|DSML|parameter> | |
| </|DSML|invoke> | |
| </|DSML|tool_calls><|end▁of▁sentence|><|User|><tool_result>[0]</tool_result><|Assistant|><think>现在开始组织回答。</think>请及时就医。<|end▁of▁sentence|> |