Instructions to use VectorNomad/arkadiko-v4-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VectorNomad/arkadiko-v4-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VectorNomad/arkadiko-v4-base")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("VectorNomad/arkadiko-v4-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use VectorNomad/arkadiko-v4-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VectorNomad/arkadiko-v4-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VectorNomad/arkadiko-v4-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VectorNomad/arkadiko-v4-base
- SGLang
How to use VectorNomad/arkadiko-v4-base 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 "VectorNomad/arkadiko-v4-base" \ --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": "VectorNomad/arkadiko-v4-base", "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 "VectorNomad/arkadiko-v4-base" \ --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": "VectorNomad/arkadiko-v4-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VectorNomad/arkadiko-v4-base with Docker Model Runner:
docker model run hf.co/VectorNomad/arkadiko-v4-base
| { | |
| "step": 9114584, | |
| "total_tokens": 100000006144, | |
| "subphase_idx": 15, | |
| "final_eval_step": 9110000, | |
| "final_overall_loss_nats": 3.3363, | |
| "final_overall_ma3_nats": 3.3602, | |
| "best_overall_loss_nats": 3.2803, | |
| "best_overall_step": 8815000, | |
| "per_domain_ma3_loss_nats": { | |
| "ar": 3.7952, | |
| "en": 3.7491, | |
| "de": 3.5717, | |
| "fr": 3.3201, | |
| "es": 3.4335, | |
| "it": 3.4953, | |
| "code": 1.9293, | |
| "math": 3.096, | |
| "classical": 3.7764 | |
| }, | |
| "per_domain_ma3_ppl": { | |
| "ar": 44.5, | |
| "en": 42.5, | |
| "de": 35.6, | |
| "fr": 27.7, | |
| "es": 30.9, | |
| "it": 32.9, | |
| "code": 6.9, | |
| "math": 22.1, | |
| "classical": 43.7 | |
| }, | |
| "training_hours": 524.7, | |
| "hardware": "NVIDIA RTX PRO 4000 Blackwell, 24GB", | |
| "wall_clock_end": "2026-05-06T14:43+00:00" | |
| } | |