- Libraries
- Transformers
How to use Rickenuel/Recommendation_PC_fast with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rickenuel/Recommendation_PC_fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Rickenuel/Recommendation_PC_fast 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 Rickenuel/Recommendation_PC_fast:Q8_0
# Run inference directly in the terminal:
llama cli -hf Rickenuel/Recommendation_PC_fast:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Rickenuel/Recommendation_PC_fast:Q8_0
# Run inference directly in the terminal:
llama cli -hf Rickenuel/Recommendation_PC_fast:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Rickenuel/Recommendation_PC_fast:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf Rickenuel/Recommendation_PC_fast:Q8_0
Use Docker
docker model run hf.co/Rickenuel/Recommendation_PC_fast:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use Rickenuel/Recommendation_PC_fast with Ollama:
ollama run hf.co/Rickenuel/Recommendation_PC_fast:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use Rickenuel/Recommendation_PC_fast with Docker Model Runner:
docker model run hf.co/Rickenuel/Recommendation_PC_fast:Q8_0
- Lemonade
How to use Rickenuel/Recommendation_PC_fast with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rickenuel/Recommendation_PC_fast:Q8_0
Run and chat with the model
lemonade run user.Recommendation_PC_fast-Q8_0
List all available models
lemonade list
- Atomic Chat
