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
- 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 new
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
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)Grok-1 GGUF Quantizations
This repository contains unofficial GGUF Quantizations of Grok-1, compatible with llama.cpp as of PR- Add grok-1 support #6204.
Updates
Native Split Support in llama.cpp
The splits have been updated to utilize the improvements from PR: llama_model_loader: support multiple split/shard GGUFs. As a result, manual merging with
gguf-splitis no longer required.With this, there is no need to merge the split files before use. Just download all splits and run llama.cpp with the first split like you would previously. It'll detect the other splits and load them as well.
Direct Split Download from huggingface using llama.cpp
Thanks to a new PR common: llama_load_model_from_url split support #6192 from phymbert it's now possible load model splits from url.
That means this downloads and runs the model:
server \
--hf-repo Arki05/Grok-1-GGUF \
--hf-file grok-1-IQ3_XS-split-00001-of-00009.gguf \
--model models/grok-1-IQ3_XS-split-00001-of-00009.gguf \
-ngl 999
And that is very cool (@phymbert)
Available Quantizations
The following Quantizations are currently available for download:
| Quant | Split Files | Size |
|---|---|---|
Q2_K |
1-of-9, 2-of-9, 3-of-9, 4-of-9, 5-of-9, 6-of-9, 7-of-9, 8-of-9, 9-of-9 | 112.4 GB |
IQ3_XS |
1-of-9, 2-of-9, 3-of-9, 4-of-9, 5-of-9, 6-of-9, 7-of-9, 8-of-9, 9-of-9 | 125.4 GB |
Q4_K |
1-of-9, 2-of-9, 3-of-9, 4-of-9, 5-of-9, 6-of-9, 7-of-9, 8-of-9, 9-of-9 | 186.0 GB |
Q6_K |
1-of-9, 2-of-9, 3-of-9, 4-of-9, 5-of-9, 6-of-9, 7-of-9, 8-of-9, 9-of-9 | 259.8 GB |
I would recommend the IQ3_XS version for now.
More Quantizations will be uploaded soon. All current Quants are created without any importance matrix.
- Downloads last month
- 33,227
1-bit
2-bit
3-bit
4-bit
6-bit
8-bit
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Arki05/Grok-1-GGUF", filename="", )