ramalama

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

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.

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.

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

sudo dnf install ramalama

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

pip install ramalama

Install RamaLama by running:

curl -fsSL https://ramalama.ai/install.sh | bash

RamaLama supports Windows with Docker Desktop or Podman Desktop:

pip install ramalama

Requirements:

• Python 3.10 or later
• Docker Desktop or Podman Desktop with WSL2 backend
• For GPU support, see NVIDIA GPU Setup for WSL2

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.

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

pip uninstall ramalama

If you installed RamaLama using DNF:

sudo dnf remove ramalama

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.

After uninstalling RamaLama using any method above, you may want to remove downloaded models and configuration files from your home directory:

# Remove downloaded models and data (can be large)
rm -rf ~/.local/share/ramalama

# Remove configuration files
rm -rf ~/.config/ramalama

# If you ran RamaLama as root, also remove:
sudo rm -rf /var/lib/ramalama

Note: The model data directory (~/.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.

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.

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

The following Intel GPUs are auto-detected by RamaLama:

See the Intel hardware table for more information.

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

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

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.

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.

• 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.

RamaLama supports multiple AI model registries types called transports.

RamaLama uses the Ollama registry transport by default

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

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.

$ cat /usr/share/ramalama/shortnames.conf
[shortnames]
  "tiny" = "ollama://tinyllama"
  "granite" = "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf"
  "granite:7b" = "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf"
  "ibm/granite" = "huggingface://instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf"
  "merlinite" = "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"
  ...

• Benchmark specified AI Model

  
  

  $ ramalama bench granite3-moe
  

• 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
  

• 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
  

• 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"
  

• 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
      },
      ...
      ]
  }
  

• 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
  

• 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.

• 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
  

• 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
  

• Pull a model

  
  

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

  $ ramalama pull granite3-moe
  

• 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
  

[!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
  

• 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
  

• 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
  

• 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
  

• Stop a running model if it is running in a container.

  
  

  $ ramalama stop mymodel
  

• Stop all running models running in containers.

  
  

  $ ramalama stop --all
  

• Print the version of RamaLama.

  
  

  $ ramalama version
  

  Returns for example:

  ramalama version 1.2.3
  

+---------------------------+
|                           |
| ramalama run granite3-moe |
|                           |
+-------+-------------------+
    |
    |
    |           +------------------+           +------------------+
    |           | Pull inferencing |           | Pull model layer |
    +-----------| runtime (cuda)   |---------->| granite3-moe     |
            +------------------+           +------------------+
                           | Repo options:    |
                           +-+-------+------+-+
                             |       |      |
                             v       v      v
                         +---------+ +------+ +----------+
                         | Hugging | | OCI  | | Ollama   |
                         | Face    | |      | | Registry |
                         +-------+-+ +---+--+ +-+--------+
                             |       |      |
                             v       v      v
                           +------------------+
                           | Start with       |
                           | cuda runtime     |
                           | and              |
                           | granite3-moe     |
                           +------------------+

Regarding this alpha, everything is under development, so expect breaking changes. If you need to reset your installation, see the Uninstall section above for instructions on removing RamaLama and cleaning up all data files, then reinstall.

• On certain versions of Python on macOS, certificates may not installed correctly, potentially causing SSL errors (e.g., when accessing huggingface.co). To resolve this, run the Install Certificates command, typically as follows:

/Applications/Python 3.x/Install Certificates.command

This project wouldn't be possible without the help of other projects like:

• llama.cpp
• vllm
• mlx-lm
• podman
• huggingface

so if you like this tool, give some of these repos a ⭐, and hey, give us a ⭐ too while you are at it.

For general questions and discussion, please use RamaLama's

Matrix

For discussions around issues/bugs and features, you can use the GitHub Issues and PRs tracking system.

We host a public community and developer meetup on Discord every other week to discuss project direction and provide an open forum for users to get help, ask questions, and showcase new features.

Join on Discord Meeting Agenda

See the full Roadmap.

Open to contributors