Instructions to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-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 mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-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 mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-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 mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-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 mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF with Ollama:
ollama run hf.co/mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF / Meta-Llama-3-8B-Instruct-function-calling-json-mode.Q6_K.gguf
- Xet hash:
- 208a0d14849b2ce556ede41312f431cc6dd2ce27efe7549553f5fe3b3be22677
- Size of remote file:
- 6.6 GB
- SHA256:
- d013c7ee8151c7d8ff310c88b93ccfc3b5be29225fdeece753ca91deb4a20acc
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