Instructions to use tarruda/Step-3.7-Flash-GGUF 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 tarruda/Step-3.7-Flash-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 tarruda/Step-3.7-Flash-GGUF:IQ4_XS # Run inference directly in the terminal: llama cli -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS # Run inference directly in the terminal: llama cli -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
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 tarruda/Step-3.7-Flash-GGUF:IQ4_XS # Run inference directly in the terminal: ./llama-cli -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
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 tarruda/Step-3.7-Flash-GGUF:IQ4_XS # Run inference directly in the terminal: ./build/bin/llama-cli -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
Use Docker
docker model run hf.co/tarruda/Step-3.7-Flash-GGUF:IQ4_XS
- LM Studio
- Jan
- vLLM
How to use tarruda/Step-3.7-Flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tarruda/Step-3.7-Flash-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": "tarruda/Step-3.7-Flash-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tarruda/Step-3.7-Flash-GGUF:IQ4_XS
- Ollama
How to use tarruda/Step-3.7-Flash-GGUF with Ollama:
ollama run hf.co/tarruda/Step-3.7-Flash-GGUF:IQ4_XS
- Unsloth Studio
How to use tarruda/Step-3.7-Flash-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 tarruda/Step-3.7-Flash-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 tarruda/Step-3.7-Flash-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tarruda/Step-3.7-Flash-GGUF to start chatting
- Pi
How to use tarruda/Step-3.7-Flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tarruda/Step-3.7-Flash-GGUF:IQ4_XS" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tarruda/Step-3.7-Flash-GGUF with Docker Model Runner:
docker model run hf.co/tarruda/Step-3.7-Flash-GGUF:IQ4_XS
- Lemonade
How to use tarruda/Step-3.7-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tarruda/Step-3.7-Flash-GGUF:IQ4_XS
Run and chat with the model
lemonade run user.Step-3.7-Flash-GGUF-IQ4_XS
List all available models
lemonade list
- Hermes Agent
How to use tarruda/Step-3.7-Flash-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 tarruda/Step-3.7-Flash-GGUF:IQ4_XS
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 tarruda/Step-3.7-Flash-GGUF:IQ4_XS
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tarruda/Step-3.7-Flash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tarruda/Step-3.7-Flash-GGUF:IQ4_XS
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 "tarruda/Step-3.7-Flash-GGUF:IQ4_XS" \ --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"
File size: 1,487 Bytes
78005e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | #!/usr/bin/env bash
set -euo pipefail
# Validate that exactly 2 arguments are provided
if [ $# -ne 2 ]; then
echo "Error: Exactly 2 arguments required."
echo "Usage: $0 <llama_cpp_dir> <gguf>"
echo "Example: $0 ~/code/llama.cpp BF16/Qwen3.5-35B-A3B-BF16-00001-of-00003.gguf"
exit 1
fi
# Assign arguments to variables for clarity
LLAMA_CPP_DIR="$1"
GGUF_PATH="$2"
# Validate that the llama.cpp directory exists
if [ ! -d "$LLAMA_CPP_DIR" ]; then
echo "Error: llama.cpp directory not found: $LLAMA_CPP_DIR"
exit 1
fi
# Validate that the Python script exists
if [ ! -e "$GGUF_PATH" ]; then
echo "Error: GGUF not found: $GGUF_PATH"
exit 1
fi
CALIBRATION_DATASET_PATH=$HOME/calibration-data.txt
if [ ! -e $CALIBRATION_DATASET_PATH ]; then
# download the calibration dataset
curl -L https://gist.githubusercontent.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a/raw/ba5b01b6960a86874592f5913e283746ff734483/ubergarm-imatrix-calibration-corpus-v02.txt > $CALIBRATION_DATASET_PATH
fi
# Get the directory where the script is located
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMATRIX_OUT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
IMATRIX_PATH="$IMATRIX_OUT_DIR/imatrix.gguf"
if [ -e "$IMATRIX_PATH" ]; then
echo "Error: imatrix already exists: $IMATRIX_PATH"
exit 1
fi
$LLAMA_CPP_DIR/build/bin/llama-imatrix \
-fit off \
--model "$GGUF_PATH"\
-f $CALIBRATION_DATASET_PATH \
-o $IMATRIX_PATH \
--ctx-size 512 \
-ub 4096 -b 4096 \
--no-mmap
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