Instructions to use mradermacher/PentaCoder-9B-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/PentaCoder-9B-i1-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/PentaCoder-9B-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/PentaCoder-9B-i1-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 mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/PentaCoder-9B-i1-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 mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/PentaCoder-9B-i1-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 mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/PentaCoder-9B-i1-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 mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/PentaCoder-9B-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use mradermacher/PentaCoder-9B-i1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/PentaCoder-9B-i1-GGUF: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": "mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mradermacher/PentaCoder-9B-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/PentaCoder-9B-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.PentaCoder-9B-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use mradermacher/PentaCoder-9B-i1-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 mradermacher/PentaCoder-9B-i1-GGUF: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 mradermacher/PentaCoder-9B-i1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mradermacher/PentaCoder-9B-i1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/PentaCoder-9B-i1-GGUF: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 "mradermacher/PentaCoder-9B-i1-GGUF: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"
Pentacoder beats qwen, ornith, and gemma hands down.
I know it’s an hybrid, but it’s the first non codex/claude model I’ve found that can complete a very complex engineering process from a design, end-to-end, without spinning into oblivion or crashing under lmstudio Bionic.
Even the open source gpt-20b didn’t survive.
Yesterday I had it running nonstop for 6 hours on my 5060Ti 16GB and it was flawless. Finished the task perfectly. My design spec is 26k, and it chewed through that in 12 seconds and never looked back. It iterated through 8 stages and 64 sub-stages like it was nothing. May not seem like a lot to many with more powerful hardware, but on limited capacity this is the best I’ve found. Would like to hear your thoughts.
Hey there, creator of the original model merge here. Just saw this comment and wanted to say thank you!
Feedback from the community like this is very valuable, because it serves as a benchmark on real-world tasks with different hardware, OS, agent harness, drivers, etc. which it turn helps me optimize my merge "recipes" for future releases.
If you're curious, I’ve got a few other hybrid merges that offer similar agentic coding capabilities you can try out:
A 9B merge of MiMo-V2.6-Distill-Qwen, Ornith-1.5 and Qwopus3.5-Coder: https://huggingface.co/collections/pragmaticcs/triumvirate-qwopus-mimo-ornith-9b-coder
A 35B-A3B Merge of Qwopus3.6-Coder, KAT-Coder-V2.5-Dev, Qwen-AgentWorld and Ornith-1.5: https://huggingface.co/collections/pragmaticcs/qwen-35b-a3b-signoffour-coder
And a huge shoutout to @mradermacher for putting together static and imatrix quants of this model!!!
Enjoy =)
let me know if need to queue something =)