Text Generation
Transformers
PyTorch
chemistry
biology
finance
legal
music
art
code
climate
medical
text-generation-inference
Merge
Mixture of Experts
Instructions to use ZeppelinCorp/Charm_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZeppelinCorp/Charm_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZeppelinCorp/Charm_10")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZeppelinCorp/Charm_10", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ZeppelinCorp/Charm_10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZeppelinCorp/Charm_10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZeppelinCorp/Charm_10", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZeppelinCorp/Charm_10
- SGLang
How to use ZeppelinCorp/Charm_10 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 "ZeppelinCorp/Charm_10" \ --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": "ZeppelinCorp/Charm_10", "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 "ZeppelinCorp/Charm_10" \ --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": "ZeppelinCorp/Charm_10", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZeppelinCorp/Charm_10 with Docker Model Runner:
docker model run hf.co/ZeppelinCorp/Charm_10
| import socket | |
| import os | |
| import logging | |
| import threading | |
| from scapy.all import sniff, IP, TCP, UDP | |
| # Configuration | |
| rules = {"192.168.1.100", "10.0.0.200"} # IPs to block | |
| blocked = {"example.com", "test.net"} # Domains to block | |
| log_path = "events.log" | |
| # Logging setup | |
| logging.basicConfig(filename=log_path, level=logging.INFO, | |
| https://www.google.com/search?q=grok+3&ie=UTF-8&oe=UTF-8&hl=en-ph&client=safari#scso=_BVy2Z4uXJsCovr0P4tq76Aw_111:496 format="%(asctime)s - %(levelname)s - %(message)s") | |
| def log_action(entry): | |
| """Log an action and print it to the console.""" | |
| logging.info(entry) | |
| print(f"[System] {entry}") | |
| def check_rule(item): | |
| """Check if an IP is in the rules set.""" | |
| return item in rules | |
| def check_data(data): | |
| """Check if any blocked domain is in the data.""" | |
| return any(item in data for item in blocked) | |
| def resolve(item): | |
| """Resolve a domain name to an IP address.""" | |
| try: | |
| return socket.gethostbyname(item) | |
| except socket.gaierror: | |
| return None | |
| def analyze(packet): | |
| """Analyze a network packet and enforce rules.""" | |
| if IP in packet: | |
| src = packet[IP].src | |
| dest = packet[IP].dst | |
| # Block traffic to/from restricted IPs | |
| if check_rule(src) or check_rule(dest): | |
| log_action(f"Blocked {src} -> {dest}") | |
| return | |
| # Check payload for blocked domains | |
| if TCP in packet or UDP in packet: | |
| content = bytes(packet[TCP].payload).decode(errors="ignore") | |
| for item in blocked: | |
| if item in content: | |
| log_action(f"Prevented access to {item} from {src}") | |
| return | |
| def restrict(item): | |
| """Block an IP address using system commands.""" | |
| try: | |
| if os.name == "nt": | |
| os.system(f"netsh advfirewall firewall add rule name='Restricted' dir=in action=block remoteip={item}") | |
| else: | |
| os.system(f"iptables -A INPUT -s {item} -j DROP") | |
| log_action(f"Restricted {item}") | |
| except Exception as e: | |
| log_action(f"Failed to restrict {item}: {e}") | |
| def monitor(): | |
| """Start packet sniffing.""" | |
| log_action("System initialized.") | |
| sniff(filter="ip", prn=analyze, store=0) | |
| if __name__ == "__main__": | |
| # Run the monitor in a separate thread | |
| monitor_thread = threading.Thread(target=monitor) | |
| monitor_thread.start() | |
| log_action("Monitoring started in a separate thread.") | |