Instructions to use leafspark/Reflection-Llama-3.1-70B-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 leafspark/Reflection-Llama-3.1-70B-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 leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
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 leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
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 leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
Use Docker
docker model run hf.co/leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "leafspark/Reflection-Llama-3.1-70B-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": "leafspark/Reflection-Llama-3.1-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
- Ollama
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with Ollama:
ollama run hf.co/leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
- Unsloth Desktop
- Pi
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
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": "leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with Docker Model Runner:
docker model run hf.co/leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
- Lemonade
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Reflection-Llama-3.1-70B-GGUF-Q4_K_S
List all available models
lemonade list
- Hermes Agent
How to use leafspark/Reflection-Llama-3.1-70B-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 leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
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 leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use leafspark/Reflection-Llama-3.1-70B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S
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 "leafspark/Reflection-Llama-3.1-70B-GGUF:Q4_K_S" \ --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"
Do we need the template?
Hi, downloading now, with big hopes. I'm curious what is this 4GB template file? I'm familiar with changing a template for ChatML, or Gemma2 or Mistral, but those are small text things, not a multi-gigabyte file?
If we do need to download both files, where does that 2nd file go? Just drop it in the same models folder?
Thanks!
The template is to show what the structure looks like (it's only supposed to contain KV data). You don't need to download it, but if you're curious you can open up the file in the HF viewer and peek inside. It's mostly just something I did to help me keep track of the model metadata when doing tensor operations. Hope this helps!
Ah, great to know. I've already got it working but hopefully this helps anyone else who might be hesitant to download such a large file.
It passed my usual set of questions, with a perfect 38/38 score, but so did the standard Instruct. It seems a little more chatty and was a pleasure to test.