Text Generation
Transformers
English
zenith
tenstorrent
code
reasoning
Mixture of Experts
ring-attention
eq-adapter
matrix-corp
Instructions to use Matrix-Corp/Zenith-7b-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matrix-Corp/Zenith-7b-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Matrix-Corp/Zenith-7b-V1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Matrix-Corp/Zenith-7b-V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Matrix-Corp/Zenith-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/Zenith-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/Zenith-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Matrix-Corp/Zenith-7b-V1
- SGLang
How to use Matrix-Corp/Zenith-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/Zenith-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/Zenith-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/Zenith-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/Zenith-7b-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Matrix-Corp/Zenith-7b-V1 with Docker Model Runner:
docker model run hf.co/Matrix-Corp/Zenith-7b-V1
File size: 5,549 Bytes
8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 8d18b7c 1ea8a03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | #!/usr/bin/env python3
"""
Push Zenith-7B model to Hugging Face Hub.
Usage:
python push_to_hf.py --repo_id Matrix-Corp/Zenith-7b-V1 --token YOUR_TOKEN
"""
import argparse
import os
import sys
from pathlib import Path
from huggingface_hub import HfApi, login, create_repo, whoami
from huggingface_hub.utils import RepositoryNotFoundError, HfHubHTTPError
def push_model(repo_id: str, token: str = None, folder_path: str = ".", private: bool = False):
"""Push model files to Hugging Face Hub with robust error handling."""
folder_path = Path(folder_path).resolve()
if not folder_path.exists():
raise ValueError(f"Folder not found: {folder_path}")
# Check required files
required_files = [
"modeling_zenith.py",
"hf_model_card.md",
"README.md",
"requirements.txt",
"train.py",
"inference.py",
"test_model.py",
"finetune_qwen.py",
"Modelfile"
]
missing = [f for f in required_files if not (folder_path / f).exists()]
if missing:
print(f"⚠️ Warning: Missing files: {missing}")
response = input("Continue anyway? (y/N): ")
if response.lower() != 'y':
return
# Authenticate
try:
if token:
login(token=token)
print("✓ Logged in with provided token")
else:
# Check if already logged in
try:
user = whoami()
print(f"✓ Already logged in as: {user['name']}")
except:
print("Please login to Hugging Face:")
login()
except Exception as e:
print(f"❌ Authentication failed: {e}")
print("\nTo get a token:")
print("1. Go to https://huggingface.co/settings/tokens")
print("2. Create a new token with 'write' permissions")
print("3. Run: python push_to_hf.py --token YOUR_TOKEN")
return
# Create API client
api = HfApi()
# Check if repo exists, create if not
try:
repo_info = api.repo_info(repo_id=repo_id, repo_type="model")
print(f"✓ Repository exists: {repo_id}")
except RepositoryNotFoundError:
print(f"📝 Repository not found. Creating: {repo_id}")
try:
create_repo(
repo_id=repo_id,
token=token,
repo_type="model",
private=private,
exist_ok=True
)
print(f"✓ Repository created")
except Exception as e:
print(f"❌ Failed to create repository: {e}")
return
except Exception as e:
print(f"⚠️ Warning: Could not check repository: {e}")
# Upload
print(f"\n📤 Uploading {folder_path} to {repo_id}...")
print("This may take a while depending on file sizes...\n")
try:
api.upload_folder(
folder_path=str(folder_path),
repo_id=repo_id,
repo_type="model",
commit_message=f"Upload Zenith-7B model"
)
print(f"\n✅ Successfully uploaded to https://huggingface.co/{repo_id}")
print("\nNext steps:")
print("1. Visit your model page")
print("2. Add a model card if needed")
print("3. Test: from transformers import AutoModel; AutoModel.from_pretrained('your-repo-id')")
except HfHubHTTPError as e:
if e.response.status_code == 401:
print(f"\n❌ Unauthorized: Invalid token or no write access")
print(" Make sure you:")
print(" - Have a valid token with 'write' permissions")
print(" - Own the organization/repository or have collaborator rights")
elif e.response.status_code == 403:
print(f"\n❌ Forbidden: You don't have permission to push to this repository")
print(" Make sure you're a member of the organization with write access")
elif e.response.status_code == 404:
print(f"\n❌ Repository not found: {repo_id}")
print(" Check the repository ID is correct")
else:
print(f"\n❌ HTTP Error {e.response.status_code}: {e}")
except Exception as e:
print(f"\n❌ Upload failed: {e}")
print("\nTroubleshooting:")
print("1. Check your internet connection")
print("2. Verify you have enough disk space")
print("3. Try logging in again: huggingface-cli login")
print("4. Check Hugging Face status: https://status.huggingface.co")
def main():
parser = argparse.ArgumentParser(description="Push Zenith-7B to Hugging Face")
parser.add_argument(
"--repo_id",
type=str,
default="Matrix-Corp/Zenith-7b-V1",
help="Hugging Face repository ID (username/model-name)"
)
parser.add_argument(
"--token",
type=str,
help="Hugging Face access token (optional if already logged in)"
)
parser.add_argument(
"--folder",
type=str,
default=".",
help="Folder containing model files (default: current directory)"
)
parser.add_argument(
"--private",
action="store_true",
help="Create repository as private (default: public)"
)
args = parser.parse_args()
push_model(args.repo_id, args.token, args.folder, args.private)
if __name__ == "__main__":
main()
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