Instructions to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp 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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
# Run inference directly in the terminal:
llama cli -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
# Run inference directly in the terminal:
llama cli -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
# Run inference directly in the terminal:
./llama-cli -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Use Docker

docker model run hf.co/jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

LM Studio
Jan

vLLM

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp",
		"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/jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Ollama
How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with Ollama:
```
ollama run hf.co/jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
```

Unsloth Studio

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp 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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp 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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp to start chatting

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

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 "jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp" \
  --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"

Docker Model Runner
How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with Docker Model Runner:
```
docker model run hf.co/jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp
```

Lemonade

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Run and chat with the model

lemonade run user.chadrock3.6-27b-coder-rocmfp4-mtp-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

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 jcbtc/chadrock3.6-27b-coder-rocmfp4-mtp

Run Hermes

hermes

Atomic Chat

chadrock3.6-27b-coder-rocmfp4-mtp

File size: 1,064 Bytes

#!/usr/bin/env bash
set -euo pipefail

server="${LLAMA_SERVER:-/path/to/rocmfp4-llama/build-strix-rocmfp4/bin/llama-server}"
model="${MODEL:-CHADROCK3.6-27B-Coder-MTP-ROCmFP4-STRIX_LEAN.gguf}"
mmproj="${MMPROJ:-mmproj-F32.mmproj}"

exec env \
  HSA_OVERRIDE_GFX_VERSION="${HSA_OVERRIDE_GFX_VERSION:-11.5.1}" \
  GGML_HIP_ENABLE_UNIFIED_MEMORY="${GGML_HIP_ENABLE_UNIFIED_MEMORY:-1}" \
  "$server" \
  -m "$model" \
  --mmproj "$mmproj" \
  --alias chadrock3.6-27b-coder \
  --host "${HOST:-127.0.0.1}" \
  --port "${PORT:-8080}" \
  --jinja \
  -c "${CTX:-262144}" \
  -ngl 999 \
  -fa on \
  -dev "${DEVICE:-ROCm0}" \
  -b 512 \
  -ub 512 \
  -t 16 \
  -tb 32 \
  -ctk q8_0 \
  -ctv q8_0 \
  --ctx-checkpoints 0 \

  --checkpoint-every-n-tokens -1 \

  --spec-type draft-mtp \
  --spec-draft-device "${SPEC_DRAFT_DEVICE:-ROCm0}" \
  --spec-draft-ngl all \
  --spec-draft-type-k q8_0 \
  --spec-draft-type-v q8_0 \
  --spec-draft-n-max 4 \
  --spec-draft-n-min 0 \
  --spec-draft-p-min 0.0 \
  --spec-draft-p-split 0.10 \
  --parallel 1 \
  --metrics \
  --no-mmap