Text Generation
GGUF
llama.cpp
ik_llama.cpp
laguna
laguna-m.1
Mixture of Experts
imatrix
poolside
conversational
Instructions to use sigargv/Laguna-M.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sigargv/Laguna-M.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sigargv/Laguna-M.1-GGUF", filename="BF16/Laguna-M.1-BF16-00001-of-00010.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sigargv/Laguna-M.1-GGUF 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 sigargv/Laguna-M.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sigargv/Laguna-M.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sigargv/Laguna-M.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sigargv/Laguna-M.1-GGUF:Q4_K_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 sigargv/Laguna-M.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sigargv/Laguna-M.1-GGUF:Q4_K_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 sigargv/Laguna-M.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sigargv/Laguna-M.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sigargv/Laguna-M.1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use sigargv/Laguna-M.1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sigargv/Laguna-M.1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sigargv/Laguna-M.1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sigargv/Laguna-M.1-GGUF:Q4_K_M
- Ollama
How to use sigargv/Laguna-M.1-GGUF with Ollama:
ollama run hf.co/sigargv/Laguna-M.1-GGUF:Q4_K_M
- Unsloth Studio
How to use sigargv/Laguna-M.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 sigargv/Laguna-M.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 sigargv/Laguna-M.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 sigargv/Laguna-M.1-GGUF to start chatting
- Pi
How to use sigargv/Laguna-M.1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sigargv/Laguna-M.1-GGUF:Q4_K_M
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": "sigargv/Laguna-M.1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sigargv/Laguna-M.1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sigargv/Laguna-M.1-GGUF:Q4_K_M
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 sigargv/Laguna-M.1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use sigargv/Laguna-M.1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sigargv/Laguna-M.1-GGUF:Q4_K_M
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 "sigargv/Laguna-M.1-GGUF:Q4_K_M" \ --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"
- Docker Model Runner
How to use sigargv/Laguna-M.1-GGUF with Docker Model Runner:
docker model run hf.co/sigargv/Laguna-M.1-GGUF:Q4_K_M
- Lemonade
How to use sigargv/Laguna-M.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sigargv/Laguna-M.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Laguna-M.1-GGUF-Q4_K_M
List all available models
lemonade list
Add files using upload-large-folder tool
Browse files- .gitattributes +10 -0
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- IQ2_XS/SHA256SUMS +10 -0
- IQ2_XS/quantize-command.txt +7 -0
- IQ2_XS/quantize.log +0 -0
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- MANIFEST.tsv +14 -14
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quant=IQ2_XS
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lane=vanilla-extra
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started_at=2026-06-21T13:16:22-04:00
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bf16_first=/home/eve/laguna-gguf/laguna-m1/bf16/Laguna-M.1-BF16-00001-of-00010.gguf
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imatrix=/mnt/pool/gguf/laguna-m1/imatrix/q8-public-v1-256-20260620-231949/laguna-m1-q8-public-v1-256-gatefix.imatrix
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threads=32
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command=/home/eve/ik_llama.cpp/build/bin/llama-quantize --keep-split --partial-requant --imatrix /mnt/pool/gguf/laguna-m1/imatrix/q8-public-v1-256-20260620-231949/laguna-m1-q8-public-v1-256-gatefix.imatrix --output-tensor-type q6_K --token-embedding-type q6_K /home/eve/laguna-gguf/laguna-m1/bf16/Laguna-M.1-BF16-00001-of-00010.gguf /home/eve/laguna-gguf/laguna-m1/quants/vector-extra-v1/IQ2_XS/Laguna-M.1-IQ2_XS-imatrix-public-v1.gguf IQ2_XS 32
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quant lane status files bytes sha256sums ppl ppl_unc kld kld_unc kl_reference completed_at log
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IQ2_XS vanilla-extra finished 10 67717449376 /home/eve/laguna-gguf/laguna-m1/quants/vector-extra-v1/IQ2_XS/SHA256SUMS pending pending pending pending pending 2026-06-21T14:32:40-04:00 /home/eve/laguna-gguf/laguna-m1/quants/vector-extra-v1/IQ2_XS/quantize.log
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IQ2_XS/Laguna-M.1-IQ2_XS-imatrix-public-v1-00008-of-00010.gguf 6828328672
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| 9 |
+
IQ2_XS/Laguna-M.1-IQ2_XS-imatrix-public-v1-00009-of-00010.gguf 6828328672
|
| 10 |
+
IQ2_XS/Laguna-M.1-IQ2_XS-imatrix-public-v1-00010-of-00010.gguf 4034921536
|
| 11 |
+
IQ2_XS/quantize-command.txt 727
|
| 12 |
+
IQ2_XS/quantize.log 173333
|
| 13 |
+
IQ2_XS/result.tsv 354
|
| 14 |
+
IQ2_XS/SHA256SUMS 1220
|