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
File size: 1,070 Bytes
6479f50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"vocab_size": 129280,
"dim": 7168,
"moe_inter_dim": 3072,
"n_layers": 61,
"n_hash_layers": 3,
"n_heads": 128,
"n_routed_experts": 384,
"n_shared_experts": 1,
"n_activated_experts": 6,
"score_func": "sqrtsoftplus",
"route_scale": 2.5,
"swiglu_limit": 10.0,
"q_lora_rank": 1536,
"head_dim": 512,
"rope_head_dim": 64,
"o_groups": 16,
"o_lora_rank": 1024,
"window_size": 128,
"original_seq_len": 65536,
"rope_theta": 10000,
"rope_factor": 16,
"beta_fast": 32,
"beta_slow": 1,
"index_n_heads": 64,
"index_head_dim": 128,
"index_topk": 1024,
"hc_mult": 4,
"hc_sinkhorn_iters": 20,
"dtype": "fp8",
"scale_fmt": "ue8m0",
"expert_dtype": "fp4",
"compress_rope_theta": 160000,
"compress_ratios": [128, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 0]
} |