Instructions to use peculiar-ragdoll/Dirk-Qwen3.8-27B-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 peculiar-ragdoll/Dirk-Qwen3.8-27B-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 peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
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 peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "peculiar-ragdoll/Dirk-Qwen3.8-27B-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": "peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
- Ollama
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with Ollama:
ollama run hf.co/peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with Docker Model Runner:
docker model run hf.co/peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
- Lemonade
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.Dirk-Qwen3.8-27B-GGUF-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "peculiar-ragdoll/Dirk-Qwen3.8-27B-GGUF:UD-Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Worth validating GSQ-RCO IQ3_S against UD-IQ3_S on Dirk?
The ISTA-DASLab GSQ-RCO results around ~3.5 bpw look unusually strong, including direct comparisons against Unsloth UD quants.
Since Dirk already includes both GSQ-RCO and UD/Dynamic variants, I'd be very interested in seeing the same comparison under your own benchmark setup.
I'd actually prefer seeing your result before spending time testing another quant locally.
References:
https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF
https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF/discussions/1
Would an IQ3_S vs comparable UD quant benchmark be something you'd consider?
Yeah I really want to run that comparison :) My GPU is busy with benchmarks for the new and improved+ablated Tiel at the moment, but DSQ-RCO validation vs Unsloth Dynamic v3 on SWE-bench-Live is in my backlog.
Yeah I really want to run that comparison :) My GPU is busy with benchmarks for the new and improved+ablated Tiel at the moment, but DSQ-RCO validation vs Unsloth Dynamic v3 on SWE-bench-Live is in my backlog.
No rush, and a thousand thanks in advance!!!