Instructions to use auswm85/flowbee-cut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use auswm85/flowbee-cut 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 auswm85/flowbee-cut:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf auswm85/flowbee-cut:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf auswm85/flowbee-cut:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf auswm85/flowbee-cut: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 auswm85/flowbee-cut:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf auswm85/flowbee-cut: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 auswm85/flowbee-cut:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf auswm85/flowbee-cut:Q4_K_M

Use Docker

docker model run hf.co/auswm85/flowbee-cut:Q4_K_M

LM Studio
Jan

vLLM

How to use auswm85/flowbee-cut with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "auswm85/flowbee-cut"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "auswm85/flowbee-cut",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/auswm85/flowbee-cut:Q4_K_M

Ollama
How to use auswm85/flowbee-cut with Ollama:
```
ollama run hf.co/auswm85/flowbee-cut:Q4_K_M
```

Unsloth Studio

How to use auswm85/flowbee-cut 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 auswm85/flowbee-cut 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 auswm85/flowbee-cut to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for auswm85/flowbee-cut to start chatting

How to use auswm85/flowbee-cut with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf auswm85/flowbee-cut: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": "auswm85/flowbee-cut:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use auswm85/flowbee-cut with Docker Model Runner:
```
docker model run hf.co/auswm85/flowbee-cut:Q4_K_M
```

Lemonade

How to use auswm85/flowbee-cut with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull auswm85/flowbee-cut:Q4_K_M

Run and chat with the model

lemonade run user.flowbee-cut-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use auswm85/flowbee-cut with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf auswm85/flowbee-cut: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 auswm85/flowbee-cut:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use auswm85/flowbee-cut with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf auswm85/flowbee-cut: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 "auswm85/flowbee-cut: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"

auswm85 commited on Jul 13

Commit

1d17486

verified ·

1 Parent(s): 817f657

release v4-da7bdcd: model card

Browse files

Files changed (1) hide show

README.md +14 -3

README.md CHANGED Viewed

@@ -10,6 +10,20 @@ tags:
   - dictation
   - speech-to-text
   - llama.cpp
 ---
 # Flowbee Cut — technical dictation cleaner
@@ -50,9 +64,6 @@ llama-server -m flowbee-cut-<version>.Q4_K_M.gguf -ngl 99 -c 4096
   directives, glossary-conditioned phonetic repairs, everyday dictation, and
   passthrough negatives. Adapter merged into the base weights, quantized to
   Q4_K_M.
-- Trained, evaluated, and published automatically by CI (GitHub Actions →
-  Modal GPU). A release is published only if it clears a held-out 22-case
-  evaluation battery; the current release scores **21/22**.
 ## Files

   - dictation
   - speech-to-text
   - llama.cpp
+model-index:
+  - name: flowbee-cut
+    results:
+      - task:
+          type: text-generation
+          name: Transcript cleaning
+        dataset:
+          name: Flowbee Cut eval battery (held-out, 22 cases)
+          type: flowbee-cut-battery
+        metrics:
+          - name: Battery pass rate
+            type: accuracy
+            value: 1.0000
+            verified: false
 ---
 # Flowbee Cut — technical dictation cleaner
   directives, glossary-conditioned phonetic repairs, everyday dictation, and
   passthrough negatives. Adapter merged into the base weights, quantized to
   Q4_K_M.
 ## Files