Instructions to use ArkAiLab-Adl/nexora-vector-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArkAiLab-Adl/nexora-vector-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArkAiLab-Adl/nexora-vector-v0.1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ArkAiLab-Adl/nexora-vector-v0.1") model = AutoModelForCausalLM.from_pretrained("ArkAiLab-Adl/nexora-vector-v0.1") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ArkAiLab-Adl/nexora-vector-v0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArkAiLab-Adl/nexora-vector-v0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArkAiLab-Adl/nexora-vector-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ArkAiLab-Adl/nexora-vector-v0.1
- SGLang
How to use ArkAiLab-Adl/nexora-vector-v0.1 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 "ArkAiLab-Adl/nexora-vector-v0.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArkAiLab-Adl/nexora-vector-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ArkAiLab-Adl/nexora-vector-v0.1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArkAiLab-Adl/nexora-vector-v0.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ArkAiLab-Adl/nexora-vector-v0.1 with Docker Model Runner:
docker model run hf.co/ArkAiLab-Adl/nexora-vector-v0.1
comfyui
Does she work at comfyui?
Do you have a workflow for this?
We haven’t officially tested Nexora-Vector-v0.1 on ComfyUI yet.
Since the model is an LLM that generates SVG/vector code, it may require custom text-generation nodes rather than a standard image workflow.
For now, it would be better suited for platforms such as LM Studio, Ollama, or llama.cpp.
A quantized GGUF version is also available here for easier local use:
https://huggingface.co/Open4bits/nexora-vector-v0.1-GGUF
We may test and share ComfyUI compatibility in the future.
Thank you very much!
You’re very welcome! Glad to help. If we test ComfyUI support in the future, we’ll share updates with the community.