Instructions to use lilbablo/humigencev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lilbablo/humigencev2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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 Studio new

How to use lilbablo/humigencev2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lilbablo/humigencev2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lilbablo/humigencev2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lilbablo/humigencev2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="lilbablo/humigencev2",
    max_seq_length=2048,
)

Docker Model Runner
How to use lilbablo/humigencev2 with Docker Model Runner:
```
docker model run hf.co/lilbablo/humigencev2
```

Humigence v2 Release

by lilbablo - opened Oct 1, 2025

Discussion

lilbablo

Owner Oct 1, 2025

•

edited Oct 1, 2025

Hi everyone!
I'm excited to announce the public release of Humigence v2, an open-source MLOps toolkit that makes supervised fine-tuning of LLMs fast, simple, and GPU-efficient. I am a complete n00b (I don't write code), but I wanted to get into the AI world and embark on some MLOps processes - starting with finetuning. It was quite a continuous back and forth. As a result, I thought it'd be nice if there was a step by step process to getting some MLOps processes done. I decided to start with fine-tuning.

Humigence wraps the Unsloth library with a user-friendly interactive CLI wizard, enabling both beginners and power users to fine-tune models on single GPU or multi-GPU (dual RTX 5090) setups with zero boilerplate code.

Features
• 🔧 Interactive CLI Wizard with Basic and Advanced modes (advanced is yet to be released)
• ⚡ Dual-GPU Training with torchrun + NCCL
• 🦥 Unsloth Integration for QLoRA/LoRA fine-tuning (4-bit & 16-bit)
• 📊 Training Summaries with loss curves, overfitting detection, and metrics
• 📁 Config Snapshots for full reproducibility
• 🖥️ Automatic GPU Detection (single or multi-GPU)
• ✅ LoRA adapter saving with optional merged weights

🔬 Requirements
• Python 3.10+
• CUDA 12.1+
• PyTorch 2.1+
• GPUs with at least 16GB VRAM (tested on dual RTX 5090s)

See requirements.txt for full dependency list.

🤝 Contributing

We welcome PRs, feedback, and issues! (Don't be too harsh lol)
👉 https://github.com/loladebabalola/humigencev2

🌍 Where to Find Us
• GitHub: loladebabalola/humigencev2
• Hugging Face Model Card: Humigence v2

🙏 Thanks

Thanks to the Hugging Face & Unsloth communities for providing the foundation that made this possible.
We hope Humigence v2 helps more people fine-tune LLMs efficiently on their own hardware!

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