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
- llama.cpp
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 โ 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, โฆ) |
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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