Text Generation
Transformers
English
vortex
science
physics
chemistry
biology
mathematics
ssm
mamba
hybrid-architecture
custom-tokenizer
from-scratch
matrix-corp
Instructions to use Matrix-Corp/Vortex-7b-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matrix-Corp/Vortex-7b-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Matrix-Corp/Vortex-7b-V1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Matrix-Corp/Vortex-7b-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Matrix-Corp/Vortex-7b-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Matrix-Corp/Vortex-7b-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Matrix-Corp/Vortex-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Matrix-Corp/Vortex-7b-V1
- SGLang
How to use Matrix-Corp/Vortex-7b-V1 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 "Matrix-Corp/Vortex-7b-V1" \ --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": "Matrix-Corp/Vortex-7b-V1", "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 "Matrix-Corp/Vortex-7b-V1" \ --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": "Matrix-Corp/Vortex-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Matrix-Corp/Vortex-7b-V1 with Docker Model Runner:
docker model run hf.co/Matrix-Corp/Vortex-7b-V1
| #!/usr/bin/env python3 | |
| import argparse | |
| import os | |
| from huggingface_hub import HfApi, create_repo | |
| def push_to_hf(repo_id, token=None, private=False): | |
| api = HfApi(token=token) | |
| # Create repo if it doesn't exist | |
| try: | |
| create_repo(repo_id, repo_type="model", private=private, exist_ok=True) | |
| print(f"Repository {repo_id} ready") | |
| except Exception as e: | |
| print(f"Repo creation note: {e}") | |
| # Upload all files in current directory | |
| cwd = os.getcwd() | |
| print(f"Uploading from {cwd} to {repo_id}...") | |
| try: | |
| api.upload_folder( | |
| folder_path=cwd, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| commit_message="Upload Vortex model" | |
| ) | |
| print(f"Successfully uploaded to {repo_id}") | |
| except Exception as e: | |
| print(f"Upload failed: {e}") | |
| raise | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--repo_id", type=str, required=True, help="HuggingFace repo ID") | |
| parser.add_argument("--token", type=str, help="HuggingFace token (optional if logged in)") | |
| parser.add_argument("--private", action="store_true", help="Make repository private") | |
| args = parser.parse_args() | |
| push_to_hf(args.repo_id, args.token, args.private) | |