Text Generation
GGUF
mistral
quantized
2-bit
3-bit
4-bit precision
5-bit
6-bit
8-bit precision
GGUF
conversational
Instructions to use MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-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 MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-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 MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_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 MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_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 MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-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": "MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
- Ollama
How to use MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
- Unsloth Desktop
- Docker Model Runner
How to use MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
- Lemonade
How to use MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.Dirty-Shirley-Writer-v0-abliterated-GGUF-Q5_K_M
List all available models
lemonade list
- Atomic Chat
Download Dirty-Shirley-Writer-v0-abliterated.Q8_0.gguf from MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 9.83 GB
-
https://huggingface.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF/resolve/main/Dirty-Shirley-Writer-v0-abliterated.Q8_0.gguf
- Command line
-
hf download hf://MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF/Dirty-Shirley-Writer-v0-abliterated.Q8_0.gguf
-
curl -L -o Dirty-Shirley-Writer-v0-abliterated.Q8_0.gguf https://huggingface.co/MaziyarPanahi/Dirty-Shirley-Writer-v0-abliterated-GGUF/resolve/main/Dirty-Shirley-Writer-v0-abliterated.Q8_0.gguf
9.83 GB
- Xet hash:
- f26de35f9f018dc98b93074a1f5e883433c3023971f0318d347396d686f130ac
- Size of remote file:
- 9.83 GB
- SHA256:
- f17ddf7b508e4e1a258e70a506102062857491e04ffbd55d14ff9ed3e52612e3
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