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 setup.py from lilbablo/humigencev2: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/lilbablo/humigencev2/resolve/main/setup.py
- Command line
-
hf download hf://lilbablo/humigencev2/setup.py
-
curl -L -o setup.py https://huggingface.co/lilbablo/humigencev2/resolve/main/setup.py
2.67 kB
| #!/usr/bin/env python3 | |
| """ | |
| Setup script for Humigence CLI | |
| """ | |
| from setuptools import setup, find_packages | |
| from pathlib import Path | |
| # Read the README file | |
| this_directory = Path(__file__).parent | |
| long_description = (this_directory / "README.md").read_text() | |
| # Read requirements | |
| with open("requirements.txt", "r", encoding="utf-8") as f: | |
| requirements = [line.strip() for line in f if line.strip() and not line.startswith("#")] | |
| setup( | |
| name="humigence", | |
| version="1.0.0", | |
| author="Humigence Team", | |
| author_email="contact@humigence.ai", | |
| description="Your AI. Your pipeline. Zero code. - Complete MLOps toolkit for LLM fine-tuning", | |
| long_description=long_description, | |
| long_description_content_type="text/markdown", | |
| url="https://github.com/your-username/humigence", | |
| project_urls={ | |
| "Bug Reports": "https://github.com/your-username/humigence/issues", | |
| "Source": "https://github.com/your-username/humigence", | |
| "Documentation": "https://github.com/your-username/humigence/wiki", | |
| }, | |
| packages=find_packages(), | |
| classifiers=[ | |
| "Development Status :: 5 - Production/Stable", | |
| "Intended Audience :: Developers", | |
| "Intended Audience :: Science/Research", | |
| "License :: OSI Approved :: MIT License", | |
| "Operating System :: OS Independent", | |
| "Programming Language :: Python :: 3", | |
| "Programming Language :: Python :: 3.8", | |
| "Programming Language :: Python :: 3.9", | |
| "Programming Language :: Python :: 3.10", | |
| "Programming Language :: Python :: 3.11", | |
| "Topic :: Scientific/Engineering :: Artificial Intelligence", | |
| "Topic :: Software Development :: Libraries :: Python Modules", | |
| "Topic :: Software Development :: User Interfaces", | |
| ], | |
| python_requires=">=3.8", | |
| install_requires=requirements, | |
| extras_require={ | |
| "dev": [ | |
| "pytest>=7.0.0", | |
| "pytest-cov>=4.0.0", | |
| "black>=22.0.0", | |
| "flake8>=5.0.0", | |
| "mypy>=1.0.0", | |
| ], | |
| "docs": [ | |
| "sphinx>=5.0.0", | |
| "sphinx-rtd-theme>=1.0.0", | |
| "myst-parser>=0.18.0", | |
| ], | |
| }, | |
| entry_points={ | |
| "console_scripts": [ | |
| "humigence=cli.main:main", | |
| ], | |
| }, | |
| include_package_data=True, | |
| package_data={ | |
| "": ["*.json", "*.yaml", "*.yml", "*.txt", "*.md"], | |
| }, | |
| keywords=[ | |
| "machine-learning", | |
| "fine-tuning", | |
| "lora", | |
| "qlora", | |
| "llm", | |
| "mlops", | |
| "cli", | |
| "wizard", | |
| "gpu", | |
| "unsloth", | |
| "transformers", | |
| "pytorch", | |
| ], | |
| zip_safe=False, | |
| ) | |