Instructions to use Jeppcode/ScalableLab2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeppcode/ScalableLab2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jeppcode/ScalableLab2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Jeppcode/ScalableLab2 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 Jeppcode/ScalableLab2:F16 # Run inference directly in the terminal: llama cli -hf Jeppcode/ScalableLab2:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Jeppcode/ScalableLab2:F16 # Run inference directly in the terminal: llama cli -hf Jeppcode/ScalableLab2:F16
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 Jeppcode/ScalableLab2:F16 # Run inference directly in the terminal: ./llama-cli -hf Jeppcode/ScalableLab2:F16
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 Jeppcode/ScalableLab2:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Jeppcode/ScalableLab2:F16
Use Docker
docker model run hf.co/Jeppcode/ScalableLab2:F16
- LM Studio
- Jan
- Ollama
How to use Jeppcode/ScalableLab2 with Ollama:
ollama run hf.co/Jeppcode/ScalableLab2:F16
- Unsloth Desktop
- Pi
How to use Jeppcode/ScalableLab2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Jeppcode/ScalableLab2:F16
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": "Jeppcode/ScalableLab2:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Jeppcode/ScalableLab2 with Docker Model Runner:
docker model run hf.co/Jeppcode/ScalableLab2:F16
- Lemonade
How to use Jeppcode/ScalableLab2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Jeppcode/ScalableLab2:F16
Run and chat with the model
lemonade run user.ScalableLab2-F16
List all available models
lemonade list
- Hermes Agent
How to use Jeppcode/ScalableLab2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Jeppcode/ScalableLab2:F16
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 Jeppcode/ScalableLab2:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Jeppcode/ScalableLab2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Jeppcode/ScalableLab2:F16
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 "Jeppcode/ScalableLab2:F16" \ --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"
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| {%- set messages = messages[1:] %} | |
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| {%- set system_message = "" %} | |
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| {#- System message + builtin tools #} | |
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| {{- "Cutting Knowledge Date: December 2023 | |
| " }} | |
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| " }} | |
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| {#- Custom tools are passed in a user message with some extra guidance #} | |
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| {%- set messages = messages[1:] %} | |
| {%- else %} | |
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| {%- endif %} | |
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| ' -}} | |
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| {{- "with its proper arguments that best answers the given prompt. | |
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| {{- "Do not use variables. | |
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| {{- " | |
| " }} | |
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| {%- endif %} | |
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| {%- elif 'tool_calls' in message %} | |
| {%- if not message.tool_calls|length == 1 %} | |
| {{- raise_exception("This model only supports single tool-calls at once!") }} | |
| {%- endif %} | |
| {%- set tool_call = message.tool_calls[0].function %} | |
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