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containers/ramalama: RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.

containers/ramalama: RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.

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RamaLama strives to make working with AI simple, straightforward, and familiar by using OCI containers.

Description

RamaLama is an open-source tool that simplifies the local use and serving of AI models for inference from any source through the familiar approach of containers. It allows engineers to use container-centric development patterns and benefits to extend to AI use cases.

RamaLama eliminates the need to configure the host system by instead pulling a container image specific to the GPUs discovered on the host system, and allowing you to work with various models and platforms.

  • Eliminates the complexity for users to configure the host system for AI.
  • Detects and pulls an accelerated container image specific to the GPUs on the host system, handling dependencies and hardware optimization.
  • RamaLama supports multiple AI model registries, including OCI Container Registries.
  • Models are treated similarly to how Podman and Docker treat container images.
  • Use common container commands to work with AI models.
  • Run AI models securely in rootless containers, isolating the model from the underlying host.
  • Keep data secure by defaulting to no network access and removing all temporary data on application exits.
  • Interact with models via REST API or as a chatbot.

Install

Install on macOS (Self-Contained Installer)

Download the self-contained macOS installer that includes Python and all dependencies:
  1. Download the latest .pkg installer from Releases
  2. Double-click to install, or run: sudo installer -pkg RamaLama-*-macOS-Installer.pkg -target /
See macOS Installation Guide for detailed instructions.

Install on Fedora

RamaLama is available in Fedora and later. To install it, run: 
sudo dnf install ramalama

Fedora Silverblue and Toolbox

On Fedora Silverblue and other immutable variants, the system is read-only. You can run RamaLama in either of these ways:
  • Toolbox: Create a Toolbox container and install RamaLama inside it (pip install ramalama or dnf install ramalama if available). Use the same Podman (or Docker) from the host so that RamaLama can start model containers; ensure the container engine is installed and that the toolbox has access to the host's container engine (e.g., by bind-mounting the socket or by configuring the toolbox to use the host's podman command).
  • Host Installation or Toolbox with Host Access: Install RamaLama on the host via rpm-ostree install ramalama if the package is available for your image, or run RamaLama from a toolbox, ensuring Podman/Docker is available and the model store is on a writable location (e.g., your home directory).
The model store defaults to ~/.local/share/ramalama, which is writable on Silverblue.

Install via PyPI

RamaLama is available via PyPI at https://pypi.org/project/ramalama 
pip install ramalama

Install script (Linux and macOS)

Install RamaLama by running: 
curl -fsSL https://ramalama.ai/install.sh | bash

Install on Windows

RamaLama supports Windows with Docker Desktop or Podman Desktop: 
pip install ramalama

Requirements:

Note: Windows support requires running containers via Docker/Podman. The model store uses hardlinks (no admin required) or falls back to file copies if hardlinks are unavailable.

Uninstall

Uninstall via pip

If you installed RamaLama using pip, you can uninstall it with: 
pip uninstall ramalama

Uninstall on Fedora

If you installed RamaLama using DNF: 
sudo dnf remove ramalama

Uninstall on macOS (Self-Contained Installer)

To remove RamaLama installed via the .pkg installer: 
# Remove the executable
sudo rm /usr/local/bin/ramalama

Remove configuration and data files

sudo rm -rf /usr/local/share/ramalama

Remove man pages (optional)

sudo rm /usr/local/share/man/man1/ramalama*.1
sudo rm /usr/local/share/man/man5/ramalama*.5
sudo rm /usr/local/share/man/man7/ramalama*.7

Remove shell completions (optional)

sudo rm /usr/local/share/bash-completion/completions/ramalama
sudo rm /usr/local/share/fish/vendor_completions.d/ramalama.fish
sudo rm /usr/local/share/zsh/site-functions/_ramalama

See the macOS Installation Guide for more details.

Remove User Data and Configuration

After uninstalling RamaLama using any method above, you may want to remove downloaded models and configuration files: 
# Remove downloaded models and data (can be large)
rm -rf -- "${XDG_DATA_HOME:-$HOME/.local/share}/ramalama"

Remove configuration files

rm -rf -- "${XDG_CONFIG_HOME:-$HOME/.config}/ramalama"

If you ran RamaLama as root, also remove:

sudo rm -rf /var/lib/ramalama

Note: The model data directory (by default ~/.local/share/ramalama) can be quite large depending on how many models you've downloaded. Make sure you want to remove these files before running the commands above.

Accelerated images

| Accelerator | Image | | :---------------------------------| :------------------------- | | GGML_VK_VISIBLE_DEVICES (or CPU) | quay.io/ramalama/ramalama | | HIP_VISIBLE_DEVICES | quay.io/ramalama/rocm | | CUDA_VISIBLE_DEVICES | quay.io/ramalama/cuda | | ASAHI_VISIBLE_DEVICES | quay.io/ramalama/asahi | | INTEL_VISIBLE_DEVICES | quay.io/ramalama/intel-gpu | | ASCEND_VISIBLE_DEVICES | quay.io/ramalama/cann | | MUSA_VISIBLE_DEVICES | quay.io/ramalama/musa |

GPU support inspection

On first run, RamaLama inspects your system for GPU support, falling back to CPU if none are present. RamaLama uses container engines like Podman or Docker to pull the appropriate OCI image with all necessary software to run an AI Model for your system setup.

How does RamaLama select the right image?

After initialization, RamaLama runs AI Models within a container based on the OCI image. RamaLama pulls container images specific to the GPUs discovered on your system. These images are tied to the minor version of RamaLama.

  • For example, RamaLama version 1.2.3 on an NVIDIA system pulls quay.io/ramalama/cuda:1.2. To override the default image, use the --image option.
RamaLama then pulls AI Models from model registries, starting a chatbot or REST API service from a simple single command. Models are treated similarly to how Podman and Docker treat container images.

Hardware Support

| Hardware | Enabled | | :--------------------------------- | :-------------------------: | | CPU | ✓ | | Apple Silicon GPU (Linux / Asahi) | ✓ | | Apple Silicon GPU (macOS) | ✓ llama.cpp or MLX | | Apple Silicon GPU (podman-machine) | ✓ | | Nvidia GPU (cuda) | ✓ See note below | | AMD GPU (rocm, vulkan) | ✓ | | Ascend NPU (Linux) | ✓ | | Intel ARC GPUs (Linux) | ✓ See note below | | Intel GPUs (vulkan / Linux) | ✓ | | Moore Threads GPU (musa / Linux) | ✓ See note below | | Windows (with Docker/Podman) | ✓ Requires WSL2 |

Nvidia GPUs

On systems with NVIDIA GPUs, see ramalama-cuda documentation for the correct host system configuration.

Intel GPUs

The following Intel GPUs are auto-detected by RamaLama:

| GPU ID | Description | | :------ | :--------------------------------- | |0xe20b | Intel® Arc™ B580 Graphics | |0xe20c | Intel® Arc™ B570 Graphics | |0x7d51 | Intel® Graphics - Arrow Lake-H | |0x7dd5 | Intel® Graphics - Meteor Lake | |0x7d55 | Intel® Arc™ Graphics - Meteor Lake |

See the Intel hardware table for more information.

Moore Threads GPUs

On systems with Moore Threads GPUs, see ramalama-musa documentation for the correct host system configuration.

MLX Runtime (macOS only)

The MLX runtime provides optimized inference for Apple Silicon Macs. MLX requires:
  • macOS operating system
  • Apple Silicon hardware (M1, M2, M3, or later)
  • Usage with --nocontainer option (containers are not supported)
  • The mlx-lm uv package installed on the host system as a uv tool
To install and run Phi-4 on MLX, use uv. If uv is not installed, you can install it with curl -LsSf https://astral.sh/uv/install.sh | sh: 
uv tool install mlx-lm

or upgrade to the latest version:

uv tool upgrade mlx-lm

ramalama --runtime=mlx serve hf://mlx-community/Unsloth-Phi-4-4bit

Default Container Engine

When both Podman and Docker are installed, RamaLama defaults to Podman. The RAMALAMA_CONTAINER_ENGINE=docker environment variable can override this behaviour. When neither are installed, RamaLama will attempt to run the model with software on the local system.

Security

Test and run your models more securely

Because RamaLama defaults to running AI models inside rootless containers using Podman or Docker, these containers isolate the AI models from information on the underlying host. With RamaLama containers, the AI model is mounted as a volume into the container in read-only mode.

This results in the process running the model (llama.cpp or vLLM) being isolated from the host. Additionally, since ramalama run uses the --network=none option, the container cannot reach the network and leak any information out of the system. Finally, containers are run with the --rm option, which means any content written during container execution is deleted when the application exits.

Here’s how RamaLama delivers a robust security footprint:

  • Container Isolation – AI models run within isolated containers, preventing direct access to the host system.
  • Read-Only Volume Mounts – The AI model is mounted in read-only mode, which means that processes inside the container cannot modify the host files.
  • No Network Access – ramalama run is executed with --network=none, meaning the model has no outbound connectivity for which information can be leaked.
  • Auto-Cleanup – Containers run with --rm, wiping out any temporary data once the session ends.
  • Drop All Linux Capabilities – No access to Linux capabilities to attack the underlying host.
  • No New Privileges – Linux Kernel feature that disables container processes from gaining additional privileges.

Transports

RamaLama supports multiple AI model registries types called transports.

Supported transports

| Transports | Web Site | | :------------- | :--------------------------------------------------- | | HuggingFace | huggingface.co | | ModelScope | modelscope.cn | | Ollama | ollama.com | | RamaLama Labs Container Registry | ramalama.com | | OCI Container Registries | opencontainers.org | | |Examples: quay.io, Docker Hub, Pulp, and Artifactory|

Default Transport

Models specified in the Hugging Face / format are automatically pulled from Hugging Face. For models specified without an organization (e.g. granite-code), RamaLama currently defaults to the Ollama transport. Note: Ollama models are no longer compatible with llama.cpp, and support for the Ollama transport will be removed in a future release. Users should migrate to Hugging Face models.

How to change transports.

Use the RAMALAMA_TRANSPORT environment variable to modify the default. export RAMALAMA_TRANSPORT=huggingface Changes RamaLama to use huggingface transport.

Individual model transports can be modified when specifying a model via the huggingface://, oci://, modelscope://, ollama://, or rlcr:// prefix.

Example: 

ramalama pull huggingface://afrideva/Tiny-Vicuna-1B-GGUF/tiny-vicuna-1b.q2_k.gguf

Transport shortnames

To make it easier for users, RamaLama uses shortname files, which contain alias names for fully specified AI Models, allowing users to refer to models using shorter names.

More information on shortnames.

RamaLama reads shortnames.conf files if they exist. These files contain a list of name-value pairs that specify the model. The following table specifies the order in which RamaLama reads the files. Any duplicate names that exist override previously defined shortnames.
| Shortnames type | Path | | :---------------- | :------------------------------------------- | | Development | ./shortnames.conf | | User (Config) | $XDG\_CONFIG\_HOME/ramalama/shortnames.conf | | User (Data) | $XDG\_DATA\_HOME/ramalama/shortnames.conf | | Administrators | /etc/ramalama/shortnames.conf | | Distribution | /usr/share/ramalama/shortnames.conf | | Local Distribution | /usr/local/share/ramalama/shortnames.conf | 

$ cat /usr/share/ramalama/shortnames.conf
[shortnames]
  "tiny" = "hf://TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
  "granite" = "hf://ibm-granite/granite-3.3-8b-instruct-GGUF"
  "granite:7b" = "hf://instructlab/granite-7b-lab-GGUF"
  "ibm/granite" = "hf://ibm-granite/granite-3.3-8b-instruct-GGUF"
  "merlinite" = "hf://instructlab/merlinite-7b-lab-GGUF"
  "merlinite:7b" = "hf://instructlab/merlinite-7b-lab-GGUF"
  ...


Commands

ramalama-bench

Benchmark specified AI Model.

Benchmark specified AI Model 
$ ramalama bench granite3-moe

ramalama-containers

List all RamaLama containers.

List all containers running AI Models 
$ ramalama containers
Returns for example: 
CONTAINER ID  IMAGE                             COMMAND               CREATED        STATUS                    PORTS                   NAMES
    85ad75ecf866  quay.io/ramalama/ramalama:latest  /usr/bin/ramalama...  5 hours ago    Up 5 hours                0.0.0.0:8080->8080/tcp  ramalama_s3Oh6oDfOP
    85ad75ecf866  quay.io/ramalama/ramalama:latest  /usr/bin/ramalama...  4 minutes ago  Exited (0) 4 minutes ago                          granite-server
List all containers in a particular format 
$ ramalama ps --noheading --format "{{ .Names }}"
Returns for example: 
ramalama_s3Oh6oDfOP
    granite-server

ramalama-convert

Convert AI Model from local storage to OCI Image.

Generate an oci model out of an Ollama model. 
$ ramalama convert ollama://tinyllama:latest oci://quay.io/rhatdan/tiny:latest
Returns for example: 
Building quay.io/rhatdan/tiny:latest...
    STEP 1/2: FROM scratch
    STEP 2/2: COPY sha256:2af3b81862c6be03c769683af18efdadb2c33f60ff32ab6f83e42c043d6c7816 /model
    --> Using cache 69db4a10191c976d2c3c24da972a2a909adec45135a69dbb9daeaaf2a3a36344
    COMMIT quay.io/rhatdan/tiny:latest
    --> 69db4a10191c
    Successfully tagged quay.io/rhatdan/tiny:latest
    69db4a10191c976d2c3c24da972a2a909adec45135a69dbb9daeaaf2a3a36344
Generate and run an OCI model with a quantized GGUF converted from Safetensors.

Generate OCI model 

$ ramalama --image quay.io/ramalama/ramalama-rag convert --gguf Q4_K_M hf://ibm-granite/granite-3.2-2b-instruct oci://quay.io/kugupta/granite-3.2-q4-k-m:latest

Returns for example: 

Converting /Users/kugupta/.local/share/ramalama/models/huggingface/ibm-granite/granite-3.2-2b-instruct to quay.io/kugupta/granite-3.2-q4-k-m:latest...
    Building quay.io/kugupta/granite-3.2-q4-k-m:latest...

Run the generated model 

$ ramalama run oci://quay.io/kugupta/granite-3.2-q4-k-m:latest

ramalama-info

Display RamaLama configuration information.

Info with no container engine. 
$ ramalama info
Returns for example: 
{
        "Accelerator": "cuda",
        "Engine": {
        "Name": ""
        },
        "Image": "quay.io/ramalama/cuda:0.7",
        "Inference": {
            "Default": "llama.cpp",
            "Engines": {
                "llama.cpp": "/usr/share/ramalama/inference-spec/engines/llama.cpp.yaml",
                "mlx": "/usr/share/ramalama/inference-spec/engines/mlx.yaml",
                "vllm": "/usr/share/ramalama/inference-spec/engines/vllm.yaml"
            },
            "Schema": {
                "1-0-0": "/usr/share/ramalama/inference-spec/schema/schema.1-0-0.json"
            }
        },
        "Shortnames": {
        "Names": {
            "cerebrum": "huggingface://froggeric/Cerebrum-1.0-7b-GGUF/Cerebrum-1.0-7b-Q4_KS.gguf",
            "deepseek": "ollama://deepseek-r1",
            "dragon": "huggingface://llmware/dragon-mistral-7b-v0/dragon-mistral-7b-q4_k_m.gguf",
            "gemma3": "hf://bartowski/google_gemma-3-4b-it-GGUF/google_gemma-3-4b-it-IQ2_M.gguf",
            "gemma3:12b": "hf://bartowski/google_gemma-3-12b-it-GGUF/google_gemma-3-12b-it-IQ2_M.gguf",
            "gemma3:1b": "hf://bartowski/google_gemma-3-1b-it-GGUF/google_gemma-3-1b-it-IQ2_M.gguf",
            "gemma3:27b": "hf://bartowski/google_gemma-3-27b-it-GGUF/google_gemma-3-27b-it-IQ2_M.gguf",
            "gemma3:4b": "hf://bartowski/google_gemma-3-4b-it-GGUF/google_gemma-3-4b-it-IQ2_M.gguf",
            "granite": "ollama://granite3.1-dense",
            "granite-code": "hf://ibm-granite/granite-3b-code-base-2k-GGUF/granite-3b-code-base.Q4_K_M.gguf",
            "granite-code:20b": "hf://ibm-granite/granite-20b-code-base-8k-GGUF/granite-20b-code-base.Q4_K_M.gguf",
            "granite-code:34b": "hf://ibm-granite/granite-34b-code-base-8k-GGUF/granite-34b-code-base.Q4_K_M.gguf",
            "granite-code:3b": "hf://ibm-granite/granite-3b-code-base-2k-GGUF/granite-3b-code-base.Q4_K_M.gguf",
            "granite-code:8b": "hf://ibm-granite/granite-8b-code-base-4k-GGUF/granite-8b-code-base.Q4_K_M.gguf",
            "granite-lab-7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
            "granite-lab-8b": "huggingface://ibm-granite/granite-8b-code-base-GGUF/granite-8b-code-base.Q4_K_M.gguf",
            "granite-lab:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
            "granite:2b": "ollama://granite3.1-dense:2b",
            "granite:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
            "granite:8b": "ollama://granite3.1-dense:8b",
            "hermes": "huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf",
            "ibm/granite": "ollama://granite3.1-dense:8b",
            "ibm/granite:2b": "ollama://granite3.1-dense:2b",
            "ibm/granite:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
            "ibm/granite:8b": "ollama://granite3.1-dense:8b",
            "merlinite": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
            "merlinite-lab-7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
            "merlinite-lab:7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
            "merlinite:7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
            "mistral": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
            "mistral:7b": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
            "mistral:7b-v1": "huggingface://TheBloke/Mistral-7B-Instruct-v0.1-GGUF/mistral-7b-instruct-v0.1.Q5_K_M.gguf",
            "mistral:7b-v2": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
            "mistral:7b-v3": "huggingface://MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF/Mistral-7B-Instruct-v0.3.Q4_K_M.gguf",
            "mistral_code_16k": "huggingface://TheBloke/Mistral-7B-Code-16K-qlora-GGUF/mistral-7b-code-16k-qlora.Q4_K_M.gguf",
            "mistral_codealpaca": "huggingface://TheBloke/Mistral-7B-codealpaca-lora-GGUF/mistral-7b-codealpaca-lora.Q4_K_M.gguf",
            "mixtao": "huggingface://MaziyarPanahi/MixTAO-7Bx2-MoE-Instruct-v7.0-GGUF/MixTAO-7Bx2-MoE-Instruct-v7.0.Q4_K_M.gguf",
            "openchat": "huggingface://TheBloke/openchat-3.5-0106-GGUF/openchat-3.5-0106.Q4_K_M.gguf",
            "openorca": "huggingface://TheBloke/Mistral-7B-OpenOrca-GGUF/mistral-7b-openorca.Q4_K_M.gguf",
            "phi2": "huggingface://MaziyarPanahi/phi-2-GGUF/phi-2.Q4_K_M.gguf",
            "smollm:135m": "ollama://smollm:135m",
            "tiny": "ollama://tinyllama"
        },
        "Files": [
            "/usr/share/ramalama/shortnames.conf",
            "/home/dwalsh/.config/ramalama/shortnames.conf",
        ]
        },
        "Store": "/usr/share/ramalama",
        "UseContainer": true,
        "Version": "0.7.5"
    }
Info with Podman engine. 
$ ramalama info
Returns for example: 
{
        "Accelerator": "cuda",
        "Engine": {
        "Info": {
            "host": {
            "arch": "amd64",
            "buildahVersion": "1.39.4",
            "cgroupControllers": [
                "cpu",
                "io",
                "memory",
                "pids"
            ],
            "cgroupManager": "systemd",
            "cgroupVersion": "v2",
            "conmon": {
                "package": "conmon-2.1.13-1.fc42.x86_64",
                "path": "/usr/bin/conmon",
                "version": "conmon version 2.1.13, commit: "
            },
            "cpuUtilization": {
                "idlePercent": 97.36,
                "systemPercent": 0.64,
                "userPercent": 2
            },
            "cpus": 32,
            "databaseBackend": "sqlite",
            "distribution": {
                "distribution": "fedora",
                "variant": "workstation",
                "version": "42"
            },
            "eventLogger": "journald",
            "freeLocks": 2043,
            "hostname": "danslaptop",
            "idMappings": {
                "gidmap": [
                {
                    "container_id": 0,
                    "host_id": 3267,
                    "size": 1
                },
                {
                    "container_id": 1,
                    "host_id": 524288,
                    "size": 65536
                }
                ],
                "uidmap": [
                {
                    "container_id": 0,
                    "host_id": 3267,
                    "size": 1
                },
                {
                    "container_id": 1,
                    "host_id": 524288,
                    "size": 65536
                }
                ]
            },
            "kernel": "6.14.2-300.fc42.x86_64",
            "linkmode": "dynamic",
            "logDriver": "journald",
            "memFree": 65281908736,
            "memTotal": 134690979840,
            "networkBackend": "netavark",
            "networkBackendInfo": {
                "backend": "netavark",
                "dns": {
                "package": "aardvark-dns-1.14.0-1.fc42.x86_64",
                "path": "/usr/libexec/podman/aardvark-dns",
                "version": "aardvark-dns 1.14.0"
                },
                "package": "netavark-1.14.1-1.fc42.x86_64",
                "path": "/usr/libexec/podman/netavark",
                "version": "netavark 1.14.1"
            },
            "ociRuntime": {
                "name": "crun",
                "package": "crun-1.21-1.fc42.x86_64",
                "path": "/usr/bin/crun",
                "version": "crun version 1.21\ncommit: 10269840aa07fb7e6b7e1acff6198692d8ff5c88\nrundir: /run/user/3267/crun\nspec: 1.0.0\n+SYSTEMD +SELINUX +APPARMOR +CAP +SECCOMP +EBPF +CRIU +LIBKRUN +WASM:wasmedge +YAJL"
            },
            "os": "linux",
            "pasta": {
                "executable": "/bin/pasta",
                "package": "passt-0^20250415.g2340bbf-1.fc42.x86_64",
                "version": ""
            },
            "remoteSocket": {
                "exists": true,
                "path": "/run/user/3267/podman/podman.sock"
            },
            "rootlessNetworkCmd": "pasta",
            "security": {
                "apparmorEnabled": false,
                "capabilities": "CAP_CHOWN,CAP_DAC_OVERRIDE,CAP_FOWNER,CAP_FSETID,CAP_KILL,CAP_NET_BIND_SERVICE,CAP_SETFCAP,CAP_SETGID,CAP_SETPCAP,CAP_SETUID,CAP_SYS_CHROOT",
                "rootless": true,
                "seccompEnabled": true,
                "seccompProfilePath": "/usr/share/containers/seccomp.json",
                "selinuxEnabled": true
            },
            "serviceIsRemote": false,
            "slirp4netns": {
                "executable": "/bin/slirp4netns",
                "package": "slirp4netns-1.3.1-2.fc42.x86_64",
                "version": "slirp4netns version 1.3.1\ncommit: e5e368c4f5db6ae75c2fce786e31eef9da6bf236\nlibslirp: 4.8.0\nSLIRP_CONFIG_VERSION_MAX: 5\nlibseccomp: 2.5.5"
            },
            "swapFree": 8589930496,
            "swapTotal": 8589930496,
            "uptime": "116h 35m 40.00s (Approximately 4.83 days)",
            "variant": ""
            },
            "plugins": {
            "authorization": null,
            "log": [
                "k8s-file",
                "none",
                "passthrough",
                "journald"
            ],
            "network": [
                "bridge",
                "macvlan",
                "ipvlan"
            ],
            "volume": [
                "local"
            ]
            },
            "registries": {
            "search": [
                "registry.fedoraproject.org",
                "registry.access.redhat.com",
                "docker.io"
            ]
            },
            "store": {
            "configFile": "/home/dwalsh/.config/containers/storage.conf",
            "containerStore": {
                "number": 5,
                "paused": 0,
                "running": 0,
                "stopped": 5
            },
            "graphDriverName": "overlay",
            "graphOptions": {},
            "graphRoot": "/usr/share/containers/storage",
            "graphRootAllocated": 2046687182848,
            "graphRootUsed": 399990419456,
            "graphStatus": {
                "Backing Filesystem": "btrfs",
                "Native Overlay Diff": "true",
                "Supports d_type": "true",
                "Supports shifting": "false",
                "Supports volatile": "true",
                "Using metacopy": "false"
            },
            "imageCopyTmpDir": "/var/tmp",
            "imageStore": {
                "number": 297
            },
            "runRoot": "/run/user/3267/containers",
            "transientStore": false,
            "volumePath": "/usr/share/containers/storage/volumes"
            },
            "version": {
            "APIVersion": "5.4.2",
            "BuildOrigin": "Fedora Project",
            "Built": 1743552000,
            "BuiltTime": "Tue Apr  1 19:00:00 2025",
            "GitCommit": "be85287fcf4590961614ee37be65eeb315e5d9ff",
            "GoVersion": "go1.24.1",
            "Os": "linux",
            "OsArch": "linux/amd64",
            "Version": "5.4.2"
            }
        },
        "Name": "podman"
        },
        "Image": "quay.io/ramalama/cuda:0.7",
        "Inference": {
            "Default": "llama.cpp",
            "Engines": {
                "llama.cpp": "/usr/share/ramalama/inference-spec/engines/llama.cpp.yaml",
                "mlx": "/usr/share/ramalama/inference-spec/engines/mlx.yaml",
                "vllm": "/usr/share/ramalama/inference-spec/engines/vllm.yaml"
            },
            "Schema": {
                "1-0-0": "/usr/share/ramalama/inference-spec/schema/schema.1-0-0.json"
            }
        },
        "Shortnames": {
            "Names": {
                "cerebrum": "huggingface://froggeric/Cerebrum-1.0-7b-GGUF/Cerebrum-1.0-7b-Q4_KS.gguf",
                "deepseek": "ollama://deepseek-r1",
                "dragon": "huggingface://llmware/dragon-mistral-7b-v0/dragon-mistral-7b-q4_k_m.gguf",
                "gemma3": "hf://bartowski/google_gemma-3-4b-it-GGUF/google_gemma-3-4b-it-IQ2_M.gguf",
                "gemma3:12b": "hf://bartowski/google_gemma-3-12b-it-GGUF/google_gemma-3-12b-it-IQ2_M.gguf",
                "gemma3:1b": "hf://bartowski/google_gemma-3-1b-it-GGUF/google_gemma-3-1b-it-IQ2_M.gguf",
                "gemma3:27b": "hf://bartowski/google_gemma-3-27b-it-GGUF/google_gemma-3-27b-it-IQ2_M.gguf",
                "gemma3:4b": "hf://bartowski/google_gemma-3-4b-it-GGUF/google_gemma-3-4b-it-IQ2_M.gguf",
                "granite": "ollama://granite3.1-dense",
                "granite-code": "hf://ibm-granite/granite-3b-code-base-2k-GGUF/granite-3b-code-base.Q4_K_M.gguf",
                "granite-code:20b": "hf://ibm-granite/granite-20b-code-base-8k-GGUF/granite-20b-code-base.Q4_K_M.gguf",
                "granite-code:34b": "hf://ibm-granite/granite-34b-code-base-8k-GGUF/granite-34b-code-base.Q4_K_M.gguf",
                "granite-code:3b": "hf://ibm-granite/granite-3b-code-base-2k-GGUF/granite-3b-code-base.Q4_K_M.gguf",
                "granite-code:8b": "hf://ibm-granite/granite-8b-code-base-4k-GGUF/granite-8b-code-base.Q4_K_M.gguf",
                "granite-lab-7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
                "granite-lab-8b": "huggingface://ibm-granite/granite-8b-code-base-GGUF/granite-8b-code-base.Q4_K_M.gguf",
                "granite-lab:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
                "granite:2b": "ollama://granite3.1-dense:2b",
                "granite:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
                "granite:8b": "ollama://granite3.1-dense:8b",
                "hermes": "huggingface://NousResearch/Hermes-2-Pro-Mistral-7B-GGUF/Hermes-2-Pro-Mistral-7B.Q4_K_M.gguf",
                "ibm/granite": "ollama://granite3.1-dense:8b",
                "ibm/granite:2b": "ollama://granite3.1-dense:2b",
                "ibm/granite:7b": "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf",
                "ibm/granite:8b": "ollama://granite3.1-dense:8b",
                "merlinite": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
                "merlinite-lab-7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
                "merlinite-lab:7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
                "merlinite:7b": "huggingface://instructlab/merlinite-7b-lab-GGUF/merlinite-7b-lab-Q4_K_M.gguf",
                "mistral": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
                "mistral:7b": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
                "mistral:7b-v1": "huggingface://TheBloke/Mistral-7B-Instruct-v0.1-GGUF/mistral-7b-instruct-v0.1.Q5_K_M.gguf",
                "mistral:7b-v2": "huggingface://TheBloke/Mistral-7B-Instruct-v0.2-GGUF/mistral-7b-instruct-v0.2.Q4_K_M.gguf",
                "mistral:7b-v3": "huggingface://MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF/Mistral-7B-Instruct-v0.3.Q4_K_M.gguf",
                "mistral_code_16k": "huggingface://TheBloke/Mistral-7B-Code-16K-qlora-GGUF/mistral-7b-code-16k-qlora.Q4_K_M.gguf",
                "mistral_codealpaca": "huggingface://TheBloke/Mistral-7B-codealpaca-lora-GGUF/mistral-7b-codealpaca-lora.Q4_K_M.gguf",
                "mixtao": "huggingface://MaziyarPanahi/MixTAO-7Bx2-MoE-Instruct-v7.0-GGUF/MixTAO-7Bx2-MoE-Instruct-v7.0.Q4_K_M.gguf",
                "openchat": "huggingface://TheBloke/openchat-3.5-0106-GGUF/openchat-3.5-0106.Q4_K_M.gguf",
                "openorca": "huggingface://TheBloke/Mistral-7B-OpenOrca-GGUF/mistral-7b-openorca.Q4_K_M.gguf",
                "phi2": "huggingface://MaziyarPanahi/phi-2-GGUF/phi-2.Q4_K_M.gguf",
                "smollm:135m": "ollama://smollm:135m",
                "tiny": "ollama://tinyllama"
            },
            "Files": [
                "/usr/share/ramalama/shortnames.conf",
                "/home/dwalsh/.config/ramalama/shortnames.conf",
            ]
        },
        "Store": "/usr/share/ramalama",
        "UseContainer": true,
        "Version": "0.7.5"
    }
Using jq to print specific ramalama info content. 
$ ramalama info |  jq .Shortnames.Names.mixtao
Returns for example: 
"huggingface://MaziyarPanahi/MixTAO-7Bx2-MoE-Instruct-v7.0-GGUF/MixTAO-7Bx2-MoE-Instruct-v7.0.Q4_K_M.gguf"

ramalama-inspect

Inspect the specified AI Model.

Inspect the smollm:135m model for basic information. 
$ ramalama inspect smollm:135m
Returns for example: 
smollm:135m
       Path: /var/lib/ramalama/models/ollama/smollm:135m
       Registry: ollama
       Format: GGUF
       Version: 3
       Endianness: little
       Metadata: 39 entries
       Tensors: 272 entries
Inspect the smollm:135m model for all information in json format. 
$ ramalama inspect smollm:135m --all --json
Returns for example: 
{
        "Name": "smollm:135m",
        "Path": "/home/mengel/.local/share/ramalama/models/ollama/smollm:135m",
        "Registry": "ollama",
        "Format": "GGUF",
        "Version": 3,
        "LittleEndian": true,
        "Metadata": {
        "general.architecture": "llama",
        "general.base_model.0.name": "SmolLM 135M",
        "general.base_model.0.organization": "HuggingFaceTB",
        "general.base_model.0.repo_url": "https://huggingface.co/HuggingFaceTB/SmolLM-135M",
        ...
        },
        "Tensors": [
        {
            "dimensions": [
            576,
            49152
            ],
            "n_dimensions": 2,
            "name": "token_embd.weight",
            "offset": 0,
            "type": 8
        },
        ...
        ]
    }

ramalama-list

List all downloaded AI Models.

You can list all models pulled into local storage. 
$ ramalama list
Returns for example: 
NAME                                                                    MODIFIED      SIZE
    ollama://smollm:135m                                                    16 hours ago  5.5M
    huggingface://afrideva/Tiny-Vicuna-1B-GGUF/tiny-vicuna-1b.q2_k.gguf     14 hours ago  460M
    ollama://moondream:latest                                               6 days ago    791M
    ollama://phi4:latest                                                    6 days ago    8.43 GB
    ollama://tinyllama:latest                                               1 week ago    608.16 MB
    ollama://granite3-moe:3b                                                1 week ago    1.92 GB
    ollama://granite3-moe:latest                                            3 months ago  1.92 GB
    ollama://llama3.1:8b                                                    2 months ago  4.34 GB
    ollama://llama3.1:latest                                                2 months ago  4.34 GB

ramalama-login

Log in to a remote registry.

Log in to quay.io/username oci registry 
$ export RAMALAMA_TRANSPORT=quay.io/username
    $ ramalama login -u username
Log in to Ollama registry 
$ export RAMALAMA_TRANSPORT=ollama
    $ ramalama login
Log in to huggingface registry 
$ export RAMALAMA_TRANSPORT=huggingface
    $ ramalama login --token=XYZ

Logging in to Hugging Face requires the hf tool. For installation and usage instructions, see the documentation of the Hugging Face command line interface.

ramalama-logout

Log out of a remote registry.

Log out from quay.io/username oci repository 
$ ramalama logout quay.io/username
Log out from Ollama registry 
$ ramalama logout ollama
Log out from huggingface 
$ ramalama logout huggingface

ramalama-perplexity

Calculate perplexity for the specified AI Model.

Calculate the perplexity of an AI Model.

Perplexity measures how well the model can predict the next token with lower values being better 

$ ramalama perplexity granite3-moe

ramalama-pull

Pull the AI Model from the Model registry to local storage.

Pull a model

You can pull a model using the pull command. By default, it pulls from the Ollama registry. 

$ ramalama pull granite3-moe

ramalama-push

Push the AI Model from local storage to a remote registry.

Push specified AI Model (OCI-only at present)

A model can from RamaLama model storage in Huggingface, Ollama, or OCI Model format. The model can also just be a model stored on disk 

$ ramalama push oci://quay.io/rhatdan/tiny:latest

ramalama-rag

Generate and convert Retrieval Augmented Generation (RAG) data from provided documents into an OCI Image.

[!NOTE]
this command does not work without a container engine.
Generate RAG data from provided documents and convert into an OCI Image.

This command uses a specific container image containing the docling tool to convert the specified content into a RAG vector database. If the image does not exist locally, RamaLama will pull the image down and launch a container to process the data.

Positional arguments:

PATH Files/Directory containing PDF, DOCX, PPTX, XLSX, HTML, AsciiDoc & Markdown formatted files to be processed. Can be specified multiple times.

IMAGE OCI Image name to contain processed rag data

ramalama rag ./README.md https://github.com/containers/podman/blob/main/README.md quay.io/ramalama/myrag
    100% |███████████████████████████████████████████████████████|  114.00 KB/    0.00 B 922.89 KB/s   59m 59s
    Building quay.io/ramalama/myrag...
    adding vectordb...
    c857ebc65c641084b34e39b740fdb6a2d9d2d97be320e6aa9439ed0ab8780fe0

The image can then be used with:

ramalama run --rag quay.io/ramalama/myrag instructlab/merlinite-7b-lab

ramalama-rm

Remove the AI Model from local storage.

Specify one or more AI Models to be removed from local storage. 
$ ramalama rm ollama://tinyllama
Remove all AI Models from local storage. 
$ ramalama rm --all

ramalama-run

Run the specified AI Model as a chatbot.

Run a chatbot on a model using the run command. By default, it pulls from the Ollama registry.

Note: RamaLama will inspect your machine for native GPU support and then will use a container engine like Podman to pull an OCI container image with the appropriate code and libraries to run the AI Model. This can take a long time to setup, but only on the first run.

$ ramalama run instructlab/merlinite-7b-lab
After the initial container image has been downloaded, you can interact with different models using the container image. 
$ ramalama run granite3-moe
Returns for example: 
> Write a hello world application in python

print("Hello World")

In a different terminal window see the running podman container. 
$ podman ps
    CONTAINER ID  IMAGE                             COMMAND               CREATED        STATUS        PORTS       NAMES
    91df4a39a360  quay.io/ramalama/ramalama:latest  /home/dwalsh/rama...  4 minutes ago  Up 4 minutes              gifted_volhard

ramalama-serve

Serve REST API on the specified AI Model.

Serve a model and connect via a browser. 
$ ramalama serve llama3
When the web UI is enabled, you can connect via your browser at: 127.0.0.1:< port > The default serving port will be 8080 if available, otherwise a free random port in the range 8081-8090. If you wish, you can specify a port to use with --port/-p.
Run two AI Models at the same time. Notice both are running within Podman Containers. 
$ ramalama serve -d -p 8080 --name mymodel ollama://smollm:135m
    09b0e0d26ed28a8418fb5cd0da641376a08c435063317e89cf8f5336baf35cfa

$ ramalama serve -d -n example --port 8081 oci://quay.io/mmortari/gguf-py-example/v1/example.gguf
    3f64927f11a5da5ded7048b226fbe1362ee399021f5e8058c73949a677b6ac9c


$ podman ps
    CONTAINER ID  IMAGE                             COMMAND               CREATED         STATUS         PORTS                   NAMES
    09b0e0d26ed2  quay.io/ramalama/ramalama:latest  /usr/bin/ramalama...  32 seconds ago  Up 32 seconds  0.0.0.0:8081->8081/tcp  ramalama_sTLNkijNNP
    3f64927f11a5  quay.io/ramalama/ramalama:latest  /usr/bin/ramalama...  17 seconds ago  Up 17 seconds  0.0.0.0:8082->8082/tcp  ramalama_YMPQvJxN97

To disable the web UI, use the --webui off flag. 
$ ramalama serve --webui off llama3

ramalama-stop

Stop the named container that is running the AI Model.

Stop a running model if it is running in a container. 
$ ramalama stop mymodel
Stop all running models running in containers. 
$ ramalama stop --all

ramalama-version

Display version of RamaLama.

Print the version of RamaLama. 
$ ramalama version
Returns for example: 
ramalama version 1.2.3

Appendix

| Command | Description | | ------------------------------------------------------ | ---------------------------------------------------------- | | ramalama(1) | primary RamaLama man page | | ramalama-bench(1)| benchmark specified AI Model | | ramalama-chat(1)| chat with specified OpenAI REST API | | ramalama-containers(1)| list all RamaLama containers | | ramalama-convert(1) | convert AI Model from local storage to OCI Image | | ramalama-info(1) | display RamaLama configuration information | | ramalama-inspect(1) | inspect the specified AI Model | | ramalama-list(1) | list all downloaded AI Models

... (README truncated for length)

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