How to use psyflowz/psyxd-nepali-jarvis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="psyflowz/psyxd-nepali-jarvis") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("psyflowz/psyxd-nepali-jarvis", device_map="auto")
How to use psyflowz/psyxd-nepali-jarvis with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "psyflowz/psyxd-nepali-jarvis" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "psyflowz/psyxd-nepali-jarvis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/psyflowz/psyxd-nepali-jarvis
How to use psyflowz/psyxd-nepali-jarvis with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "psyflowz/psyxd-nepali-jarvis" \ --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": "psyflowz/psyxd-nepali-jarvis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "psyflowz/psyxd-nepali-jarvis" \ --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": "psyflowz/psyxd-nepali-jarvis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use psyflowz/psyxd-nepali-jarvis with Docker Model Runner:
