Instructions to use professorf/Llama-3.1-8B-Instruct-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 professorf/Llama-3.1-8B-Instruct-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 professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf professorf/Llama-3.1-8B-Instruct-gguf: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 professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf professorf/Llama-3.1-8B-Instruct-gguf: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 professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
Use Docker
docker model run hf.co/professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use professorf/Llama-3.1-8B-Instruct-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "professorf/Llama-3.1-8B-Instruct-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": "professorf/Llama-3.1-8B-Instruct-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
- Ollama
How to use professorf/Llama-3.1-8B-Instruct-gguf with Ollama:
ollama run hf.co/professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
- Unsloth Studio
How to use professorf/Llama-3.1-8B-Instruct-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 professorf/Llama-3.1-8B-Instruct-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 professorf/Llama-3.1-8B-Instruct-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for professorf/Llama-3.1-8B-Instruct-gguf to start chatting
- Pi
How to use professorf/Llama-3.1-8B-Instruct-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf professorf/Llama-3.1-8B-Instruct-gguf: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": "professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use professorf/Llama-3.1-8B-Instruct-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf professorf/Llama-3.1-8B-Instruct-gguf: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 "professorf/Llama-3.1-8B-Instruct-gguf: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"
- Docker Model Runner
How to use professorf/Llama-3.1-8B-Instruct-gguf with Docker Model Runner:
docker model run hf.co/professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
- Lemonade
How to use professorf/Llama-3.1-8B-Instruct-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.1-8B-Instruct-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use professorf/Llama-3.1-8B-Instruct-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 professorf/Llama-3.1-8B-Instruct-gguf: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 professorf/Llama-3.1-8B-Instruct-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
ProfessorF is Nick V. Flor, PhD
Models quantized for research reproducibility purposes
💫 Community Model> Llama 3.1 8B Instruct by Meta
👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.
Model creator: meta-llama
Original model: Meta-Llama-3.1-8B-Instruct
GGUF quantization: provided by professorf based on llama.cpp release b3472.
ProfessorF (Nick V. Flor, PhD): Quantizes models for research reproducibility. If referenced in a paper, this is the exact quantized model used in that research.The quantization was done for research reproducibility purposes.
Model Summary:
Llama 3.1 is an update to the previously released family of Llama 3 models. It has improved performance across the board, especially in multilingual tasks.
It is the current state of the art for open source and can be used for basically any task you throw at it.
Prompt Template:
Choose the 'Llama 3' preset in your client.
Under the hood, the model will see a prompt that's formatted like so:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Technical Details
Llama 3.1 features an improved 128k context window.
It has been trained on 15T tokens, including 25 million synthetically generated samples.
For more details, check their blog post here
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!
Disclaimers
ProfessorF is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. ProfessorF does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. ProfessorF may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. ProfessorF disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. ProfessorF further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through ProfessorF.
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Model tree for professorf/Llama-3.1-8B-Instruct-gguf
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meta-llama/Llama-3.1-8B