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
| { | |
| "tokenizer_class": "LlamaTokenizer", | |
| "model_max_length": 2048, | |
| "added_tokens": { | |
| "<unk>": 0, | |
| "<bos>": 1, | |
| "<eos>": 2, | |
| "<pad>": 3, | |
| "<system>": 7, | |
| "<user>": 8, | |
| "<assistant>": 9, | |
| "<think>": 10, | |
| "</think>": 11, | |
| "<tool_call>": 12, | |
| "<tool_result>": 13, | |
| "<eot>": 14, | |
| "<mask>": 4, | |
| "<sep>": 5, | |
| "<cls>": 6 | |
| }, | |
| "_arkadiko_note": "The trained model config (config.json) sets bos_token_id=0, eos_token_id=2, pad_token_id=1. The actual SPM model ships <unk>=0, <bos>=1, <eos>=2, <pad>=3. The runtime SHOULD use the tokenizer-derived IDs (this file's `added_tokens`) — config.json values are kept as-trained for reproducibility but are misaligned. See README for details." | |
| } | |