Instructions to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF with Ollama:
ollama run hf.co/kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-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 kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingabzpro/llama-3-8b-chat-doctor-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.llama-3-8b-chat-doctor-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Xet hash:
- f06765f20acca78940a8cece89dc6ac6e5c830048e4be0ef6a53f1a6351385f6
- Size of remote file:
- 4.92 GB
- SHA256:
- fbfedb38ac28a582216d52c89f51215d4c3ff3ce3cb7a4c4bdfe320f370a8553
路
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