Instructions to use vorenthiclabs/vorenthos-glare-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vorenthiclabs/vorenthos-glare-2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf vorenthiclabs/vorenthos-glare-2 # Run inference directly in the terminal: llama cli -hf vorenthiclabs/vorenthos-glare-2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf vorenthiclabs/vorenthos-glare-2 # Run inference directly in the terminal: llama cli -hf vorenthiclabs/vorenthos-glare-2
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf vorenthiclabs/vorenthos-glare-2 # Run inference directly in the terminal: ./llama-cli -hf vorenthiclabs/vorenthos-glare-2
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf vorenthiclabs/vorenthos-glare-2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf vorenthiclabs/vorenthos-glare-2
Use Docker
docker model run hf.co/vorenthiclabs/vorenthos-glare-2
- LM Studio
- Jan
- vLLM
How to use vorenthiclabs/vorenthos-glare-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vorenthiclabs/vorenthos-glare-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vorenthiclabs/vorenthos-glare-2", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/vorenthiclabs/vorenthos-glare-2
- Ollama
How to use vorenthiclabs/vorenthos-glare-2 with Ollama:
ollama run hf.co/vorenthiclabs/vorenthos-glare-2
- Unsloth Studio
How to use vorenthiclabs/vorenthos-glare-2 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 vorenthiclabs/vorenthos-glare-2 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 vorenthiclabs/vorenthos-glare-2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vorenthiclabs/vorenthos-glare-2 to start chatting
- Pi
How to use vorenthiclabs/vorenthos-glare-2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vorenthiclabs/vorenthos-glare-2
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "vorenthiclabs/vorenthos-glare-2" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use vorenthiclabs/vorenthos-glare-2 with Docker Model Runner:
docker model run hf.co/vorenthiclabs/vorenthos-glare-2
- Lemonade
How to use vorenthiclabs/vorenthos-glare-2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vorenthiclabs/vorenthos-glare-2
Run and chat with the model
lemonade run user.vorenthos-glare-2-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use vorenthiclabs/vorenthos-glare-2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vorenthiclabs/vorenthos-glare-2
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default vorenthiclabs/vorenthos-glare-2
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use vorenthiclabs/vorenthos-glare-2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf vorenthiclabs/vorenthos-glare-2
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "vorenthiclabs/vorenthos-glare-2" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
vorenthos-glare-2 — Ollama Export
🖼️ Vision-Language Model — accepts both image and text inputs.
Exported from a local Ollama installation and uploaded to the Hugging Face Hub by vorenthiclabs.
Model Details
| Field | Value |
|---|---|
| Base model | vorenthos-glare-2 |
| Tag / variant | latest |
| Model type | Vision-Language (Multimodal) |
| Format | GGUF (llama.cpp-compatible) |
| Total size | 6.19 GB |
| Layers | 7 |
Quick Start
With Ollama (recommended)
ollama pull vorenthos-glare-2
ollama run vorenthos-glare-2
With llama.cpp / llama-cpp-python (GGUF)
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="vorenthiclabs/vorenthos-glare-2",
filename="*.gguf",
)
output = llm("Hello, who are you?", max_tokens=256)
print(output["choices"][0]["text"])
Vision input example (llama-cpp-python)
from llama_cpp import Llama
from llama_cpp.llama_chat_format import MoondreamChatHandler # adjust handler per model
llm = Llama.from_pretrained(
repo_id="vorenthiclabs/vorenthos-glare-2",
filename="*.gguf",
chat_handler=MoondreamChatHandler(clip_model_path="mmproj*.gguf"),
n_ctx=4096,
)
response = llm.create_chat_completion(messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
{"type": "text", "text": "Describe this image."}
]
}])
print(response["choices"][0]["message"]["content"])
With Hugging Face transformers + GGUF support
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("vorenthiclabs/vorenthos-glare-2")
model = AutoModelForCausalLM.from_pretrained("vorenthiclabs/vorenthos-glare-2")
File Structure
| File | Description |
|---|---|
config.json |
Ollama model configuration / metadata |
model-*.gguf |
Quantised weights in GGUF format |
tokenizer.jinja |
Chat template |
params.json |
Generation parameters (temperature, top-p, …) |
system_prompt.txt |
Default system prompt embedded in the model |
License
Please check the original model's license before redistribution.
This upload is provided as-is for research and experimentation.
About vorenthiclabs
Visit us at https://huggingface.co/vorenthiclabs.
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