Text Generation
Transformers
English
finetuning
lora
qlora
unsloth
gpu
distributed
Eval Results (legacy)
Instructions to use lilbablo/humigencev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilbablo/humigencev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lilbablo/humigencev2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilbablo/humigencev2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lilbablo/humigencev2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lilbablo/humigencev2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lilbablo/humigencev2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lilbablo/humigencev2
- SGLang
How to use lilbablo/humigencev2 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 "lilbablo/humigencev2" \ --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": "lilbablo/humigencev2", "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 "lilbablo/humigencev2" \ --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": "lilbablo/humigencev2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use lilbablo/humigencev2 with Docker Model Runner:
docker model run hf.co/lilbablo/humigencev2
Download cli/simple_main.py from lilbablo/humigencev2: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/lilbablo/humigencev2/resolve/main/cli/simple_main.py
- Command line
-
hf download hf://lilbablo/humigencev2/cli/simple_main.py
-
curl -L -o simple_main.py https://huggingface.co/lilbablo/humigencev2/resolve/main/cli/simple_main.py
1.48 kB
| #!/usr/bin/env python3 | |
| import typer | |
| from rich.console import Console | |
| import sys | |
| from pathlib import Path | |
| # Add the parent directory to the path so we can import from cli | |
| sys.path.insert(0, str(Path(__file__).parent.parent)) | |
| app = typer.Typer() | |
| console = Console() | |
| def main(ctx: typer.Context): | |
| if ctx.invoked_subcommand is None: | |
| console.print("[bold cyan]Humigence — Your AI. Your pipeline. Zero code.[/bold cyan]") | |
| console.print("[green]A complete MLOps suite built for makers, teams, and enterprises.[/green]") | |
| console.print() | |
| console.print("Options:") | |
| console.print("1. Supervised Fine-Tuning ✅") | |
| console.print("2. RAG Implementation (coming soon)") | |
| console.print("3. EnterpriseGPT (coming soon)") | |
| console.print("4. Batch Inference (coming soon)") | |
| console.print("5. Context Length (coming soon)") | |
| console.print() | |
| console.print("Starting Supervised Fine-Tuning...") | |
| # Simple test without interactive prompts | |
| console.print("[yellow]Testing system detection...[/yellow]") | |
| try: | |
| from utils.device import get_system_info | |
| info = get_system_info() | |
| console.print(f"✅ System detected: {info['Platform']} with {info['GPU Count']} GPUs") | |
| except Exception as e: | |
| console.print(f"❌ System detection failed: {e}") | |
| if __name__ == "__main__": | |
| app() | |