Instructions to use vorenthiclabs/Vorenthos-Instruct-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vorenthiclabs/Vorenthos-Instruct-7b 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-Instruct-7b
# Run inference directly in the terminal:
llama cli -hf vorenthiclabs/Vorenthos-Instruct-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vorenthiclabs/Vorenthos-Instruct-7b
# Run inference directly in the terminal:
llama cli -hf vorenthiclabs/Vorenthos-Instruct-7b

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-Instruct-7b
# Run inference directly in the terminal:
./llama-cli -hf vorenthiclabs/Vorenthos-Instruct-7b

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-Instruct-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vorenthiclabs/Vorenthos-Instruct-7b

Use Docker

docker model run hf.co/vorenthiclabs/Vorenthos-Instruct-7b

LM Studio
Jan

vLLM

How to use vorenthiclabs/Vorenthos-Instruct-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vorenthiclabs/Vorenthos-Instruct-7b"
# 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-Instruct-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vorenthiclabs/Vorenthos-Instruct-7b

Ollama
How to use vorenthiclabs/Vorenthos-Instruct-7b with Ollama:
```
ollama run hf.co/vorenthiclabs/Vorenthos-Instruct-7b
```

Unsloth Studio

How to use vorenthiclabs/Vorenthos-Instruct-7b 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-Instruct-7b 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-Instruct-7b 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-Instruct-7b to start chatting

Docker Model Runner
How to use vorenthiclabs/Vorenthos-Instruct-7b with Docker Model Runner:
```
docker model run hf.co/vorenthiclabs/Vorenthos-Instruct-7b
```

Lemonade

How to use vorenthiclabs/Vorenthos-Instruct-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vorenthiclabs/Vorenthos-Instruct-7b

Run and chat with the model

lemonade run user.Vorenthos-Instruct-7b-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Vorenthos-Instruct-7b — Ollama Export

Exported from a local Ollama installation and uploaded to the Hugging Face Hub by vorenthiclabs.

Model Details

Field	Value
Base model	`Vorenthos-Instruct-7b`
Tag / variant	`latest`
Model type	Text Generation
Format	GGUF (llama.cpp-compatible)
Total size	4.37 GB
Layers	4

Quick Start

With Ollama (recommended)

ollama pull Vorenthos-Instruct-7b
ollama run Vorenthos-Instruct-7b

With llama.cpp / llama-cpp-python (GGUF)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="vorenthiclabs/Vorenthos-Instruct-7b",
    filename="*.gguf",
)
output = llm("Hello, who are you?", max_tokens=256)
print(output["choices"][0]["text"])

With Hugging Face `transformers` + GGUF support

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("vorenthiclabs/Vorenthos-Instruct-7b")
model     = AutoModelForCausalLM.from_pretrained("vorenthiclabs/Vorenthos-Instruct-7b")

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.

Downloads last month: 109

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants