Instructions to use alst10/PentaCoder-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use alst10/PentaCoder-9B 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 alst10/PentaCoder-9B:Q2_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

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 alst10/PentaCoder-9B:Q2_0_ROCMFPX
# Run inference directly in the terminal:
./llama-cli -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

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 alst10/PentaCoder-9B:Q2_0_ROCMFPX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

Use Docker

docker model run hf.co/alst10/PentaCoder-9B:Q2_0_ROCMFPX

LM Studio
Jan

Ollama

How to use alst10/PentaCoder-9B with Ollama:

ollama run hf.co/alst10/PentaCoder-9B:Q2_0_ROCMFPX

Unsloth Desktop

How to use alst10/PentaCoder-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

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": "alst10/PentaCoder-9B:Q2_0_ROCMFPX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alst10/PentaCoder-9B with Docker Model Runner:
```
docker model run hf.co/alst10/PentaCoder-9B:Q2_0_ROCMFPX
```

Lemonade

How to use alst10/PentaCoder-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alst10/PentaCoder-9B:Q2_0_ROCMFPX

Run and chat with the model

lemonade run user.PentaCoder-9B-Q2_0_ROCMFPX

List all available models

lemonade list

Hermes Agent

How to use alst10/PentaCoder-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

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 alst10/PentaCoder-9B:Q2_0_ROCMFPX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alst10/PentaCoder-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/PentaCoder-9B:Q2_0_ROCMFPX

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 "alst10/PentaCoder-9B:Q2_0_ROCMFPX" \
  --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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

        ---
        license: cc-by-4.0
        base_model: pragmaticcs/PentaCoder-9B
        tags:
        - gguf
        - rocm
        - rocmfpx
        - amd
        - llama.cpp
        ---

        # PentaCoder-9B - ROCmFPX Quantized

        This repository contains AMD ROCmFPX quantized GGUF weights converted from [pragmaticcs/PentaCoder-9B](https://huggingface.co/pragmaticcs/PentaCoder-9B) using [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX).

        ## Available Files

        | File | Size | Format |

| --- | --- | --- | | PentaCoder-9B-Q4_0_ROCMFP4_FAST.gguf | 4.44 GB | Q4_0_ROCMFP4_FAST | | PentaCoder-9B-Q4_0_ROCMFP4.gguf | 5.47 GB | Q4_0_ROCMFP4 | | PentaCoder-9B-Q4_0_ROCMFP4_COHERENT.gguf | 4.95 GB | Q4_0_ROCMFP4_COHERENT | | PentaCoder-9B-Q4_0_ROCMFP4_FAST_COHERENT.gguf | 4.72 GB | Q4_0_ROCMFP4_FAST_COHERENT | | PentaCoder-9B-Q4_0_ROCMFP4_LEAN.gguf | 4.82 GB | Q4_0_ROCMFP4_LEAN | | PentaCoder-9B-Q4_0_ROCMFP4_STRIX.gguf | 4.74 GB | Q4_0_ROCMFP4_STRIX | | PentaCoder-9B-Q4_0_ROCMFP4_STRIX_LEAN.gguf | 4.62 GB | Q4_0_ROCMFP4_STRIX_LEAN | | PentaCoder-9B-Q2_0_ROCMFPX.gguf | 3.10 GB | Q2_0_ROCMFPX | | PentaCoder-9B-Q3_0_ROCMFPX.gguf | 4.60 GB | Q3_0_ROCMFPX | | PentaCoder-9B-Q6_0_ROCMFPX.gguf | 6.86 GB | Q6_0_ROCMFPX | | PentaCoder-9B-Q8_0_ROCMFPX.gguf | 8.61 GB | Q8_0_ROCMFPX | | PentaCoder-9B-Q8_0_ROCMFPX_AGENT.gguf | 8.77 GB | Q8_0_ROCMFPX_AGENT |

        ## Usage Instructions with ROCmFPX llama.cpp

        ```bash
        git clone --depth 1 [https://github.com/charlie12345/ROCmFPX.git](https://github.com/charlie12345/ROCmFPX.git)
        cd ROCmFPX
        cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=OFF -DGGML_NATIVE=ON
        cmake --build build --target llama-cli -j

        ./build/bin/llama-cli -m PentaCoder-9B-Q4_0_ROCMFP4_FAST.gguf -p "You are a helpful assistant. Hello!" -n 128
        ```

        ## License
        This model is distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

4-bit

8-bit

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