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

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

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 @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": "tarruda/Step-3.7-Flash-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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"

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

Reasoning Budget Message=?

by BingoBird - opened 26 days ago

Discussion

BingoBird

26 days ago

•

edited 26 days ago

llama-server --no-mmap --no-warmup --model $model --mmproj $mmproj
--ctx-size $ctx_size -np $parallel --temp 1.0 --top-p 0.95
--repeat-penalty 1.0 --presence-penalty 0.0
--reasoning-budget-message "$reasoning_budget_message"
--reasoning-preserve
--spec-type draft-mtp -md $mtp --spec-draft-n-max 3 --spec-draft-p-min 0.65
-ctxcp 8 --checkpoint-min-step 512
--cache-ram 4096

What goes into "$reasoning_budget_message"?

sorry if it's a stupid question. 😄

tarruda

Owner 26 days ago

It is defined earlier in the script: https://huggingface.co/tarruda/Step-3.7-Flash-GGUF#usage

BingoBird

26 days ago

Somehow the multi-line variable assignment confused me. Thank you very much for explanation. Closing.

BingoBird changed discussion status to closed 26 days ago

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