Instructions to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF", filename="darkidol-llama-3.1-8b-instruct-1.2-uncensored-q3_k_s.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama-cli -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama-cli -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_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 riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./llama-cli -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_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 riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
Use Docker
docker model run hf.co/riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
- LM Studio
- Jan
- vLLM
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-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": "riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
- Ollama
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with Ollama:
ollama run hf.co/riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
- Unsloth Studio new
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-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 riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-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 riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF to start chatting
- Docker Model Runner
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with Docker Model Runner:
docker model run hf.co/riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
- Lemonade
How to use riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S
Run and chat with the model
lemonade run user.DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF-Q3_K_S
List all available models
lemonade list
Run and chat with the model
lemonade run user.DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF-Q3_K_SList all available models
lemonade listriqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF
This model was converted to GGUF format from aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF --hf-file darkidol-llama-3.1-8b-instruct-1.2-uncensored-q3_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF --hf-file darkidol-llama-3.1-8b-instruct-1.2-uncensored-q3_k_s.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF --hf-file darkidol-llama-3.1-8b-instruct-1.2-uncensored-q3_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF --hf-file darkidol-llama-3.1-8b-instruct-1.2-uncensored-q3_k_s.gguf -c 2048
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Pull the model
# Download Lemonade from https://lemonade-server.ai/lemonade pull riqalter/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored-Q3_K_S-GGUF:Q3_K_S