Instructions to use Arki05/Grok-1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arki05/Grok-1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Arki05/Grok-1-GGUF", dtype="auto") - llama-cpp-python
How to use Arki05/Grok-1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Arki05/Grok-1-GGUF", filename="IQ1_M/grok-1-IQ1_M-00001-of-00009.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Arki05/Grok-1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Arki05/Grok-1-GGUF:IQ1_M # Run inference directly in the terminal: llama-cli -hf Arki05/Grok-1-GGUF:IQ1_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Arki05/Grok-1-GGUF:IQ1_M # Run inference directly in the terminal: llama-cli -hf Arki05/Grok-1-GGUF:IQ1_M
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 Arki05/Grok-1-GGUF:IQ1_M # Run inference directly in the terminal: ./llama-cli -hf Arki05/Grok-1-GGUF:IQ1_M
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 Arki05/Grok-1-GGUF:IQ1_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Arki05/Grok-1-GGUF:IQ1_M
Use Docker
docker model run hf.co/Arki05/Grok-1-GGUF:IQ1_M
- LM Studio
- Jan
- Ollama
How to use Arki05/Grok-1-GGUF with Ollama:
ollama run hf.co/Arki05/Grok-1-GGUF:IQ1_M
- Unsloth Studio
How to use Arki05/Grok-1-GGUF 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 Arki05/Grok-1-GGUF 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 Arki05/Grok-1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Arki05/Grok-1-GGUF to start chatting
- Docker Model Runner
How to use Arki05/Grok-1-GGUF with Docker Model Runner:
docker model run hf.co/Arki05/Grok-1-GGUF:IQ1_M
- Lemonade
How to use Arki05/Grok-1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Arki05/Grok-1-GGUF:IQ1_M
Run and chat with the model
lemonade run user.Grok-1-GGUF-IQ1_M
List all available models
lemonade list
Ctrl+K
- IQ1_M
- IQ1_S
- IQ2_M
- IQ2_S
- IQ2_XS
- IQ2_XXS
- IQ3_M
- IQ3_S
- IQ3_XS
- IQ3_XXS
- IQ4_NL
- IQ4_XS
- Q2_K
- Q2_K_S
- Q3_K_L
- Q3_K_M
- Q3_K_S
- Q4_K
- Q5_K
- Q6_K
- Q8_0
- 1.56 kB
- 5.45 kB
- 29 Bytes
- 17.5 GB xet
- 15.3 GB xet
- 15.2 GB xet
- 15.6 GB xet
- 15.4 GB xet
- 15.2 GB xet
- 15.2 GB xet
- 16 GB xet
- 4.18 GB xet
- 14.9 GB xet
- 13.8 GB xet
- 13.8 GB xet
- 14.2 GB xet
- 14 GB xet
- 13.8 GB xet
- 13.8 GB xet
- 14.1 GB xet
- 3.6 GB xet
- 25.9 GB xet
- 22.3 GB xet
- 22.6 GB xet
- 22.8 GB xet
- 22.9 GB xet
- 22.4 GB xet
- 22.4 GB xet
- 24.7 GB xet