run

Synopsis

ramalama run [options] model [arg ...]

MODEL TRANSPORTS

Transports	Prefix	Web Site
URL based	https://, http://, file://	https://web.site/ai.model, file:///tmp/ai.model
HuggingFace	huggingface://, hf://, hf.co/	huggingface.co
ModelScope	modelscope://, ms://	modelscope.cn
Ollama	ollama://	ollama.com
rlcr	rlcr://	ramalama.com
OCI Container Registries	oci://, docker://	opencontainers.org
Hosted API Providers	openai://	api.openai.com
RamaLama defaults to the Ollama registry transport. This default can be overridden in the ramalama.conf file or via the
RAMALAMA_TRANSPORT environment variable. For example, export RAMALAMA_TRANSPORT=huggingface changes RamaLama to use the Hugging Face transport.

Modify individual model transports by specifying the huggingface://, oci://, ollama://, https://, http://, or file:// prefix to the model.

URL support means if a model is on a web site or even on your local system, you can run it directly.

Hosted API transports connect directly to the remote provider and bypass the local container runtime. In this mode, flags that tune local containers (for example --image, GPU settings, or --network) do not apply, and the provider's own capabilities and security posture govern the execution. URL support means if a model is on a web site or even on your local system, you can run it directly.

Options

--api=llama-stack | none**

Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.(default: none) The default can be overridden in the ramalama.conf file.

--authfile=path

Path to the authentication file for OCI registries.

--backend=auto | vulkan | rocm | cuda | sycl | openvino | cann | musa

GPU backend to use for inference (default: auto).

Available backends depend on the detected GPU hardware.

auto (default): Automatically selects the preferred backend based on your GPU:

• AMD GPUs: vulkan (Linux/macOS) or rocm (Windows)
• NVIDIA GPUs: cuda
• Intel GPUs: vulkan (Linux/macOS) or sycl (Windows); openvino available as explicit option
• Ascend NPUs: cann
• MUSA GPUs: musa
• No GPU: vulkan (CPU fallback)

Platform-specific behavior:

• On Linux/macOS, Vulkan provides broad compatibility and is preferred for AMD and Intel GPUs
• On Windows, vulkan is not supported on WSL2, so vendor-specific backends (rocm, sycl) are preferred

Explicit backend selection:

• vulkan: Use Vulkan-based inference (compatible with AMD, Intel, and CPU)
• rocm: Use AMD ROCm backend (AMD GPUs only)
• cuda: Use NVIDIA CUDA backend (NVIDIA GPUs only)
• sycl: Use Intel SYCL/oneAPI backend (Intel GPUs only)
• openvino: Use Intel OpenVINO backend (Intel GPUs only); uses quay.io/ramalama/openvino
• cann: Use Huawei CANN backend (Ascend NPUs only); uses quay.io/ramalama/cann
• musa: Use Moore Threads MUSA backend (MUSA GPUs only); uses quay.io/ramalama/musa

Available choices: The allowed values for --backend are dynamically determined based on your detected GPU hardware. For example, on a system with an AMD GPU, only auto, vulkan, and rocm are available.

Configuration: The default can be overridden in the ramalama.conf file or via the RAMALAMA_BACKEND environment variable.

Examples:

# Use auto-detection (default)
ramalama run granite

# Force Vulkan backend
ramalama run --backend vulkan granite

# Force ROCm backend on AMD GPU
ramalama run --backend rocm granite

--cache-reuse=BYTES

Minimum chunk size (in bytes) to attempt reusing from the cache via KV shifting. When omitted, llama-server uses its built-in default.

--color

Indicate whether to use color in the chat. Possible values are "never", "always" and "auto". (default: auto)

--ctx-size, -c

size of the prompt context. This option is also available as --max-model-len. Applies to llama.cpp and vllm regardless of alias (default: 0, 0 = loaded from model)

--device

Add a host device to the container. Optional permissions parameter can be used to specify device permissions by combining r for read, w for write, and m for mknod(2).

Example: --device=/dev/dri/renderD128:/dev/xvdc:rwm

The device specification is passed directly to the underlying container engine. See documentation of the supported container engine for more information.

Pass '--device=none' to explicitly add no device to the container, e.g., for running a CPU-only performance comparison.

--env=

Set environment variables inside the container.

This option allows arbitrary environment variables that are available for the process to be launched inside the container. If an environment variable is specified without a value, the container engine checks the host environment for a value and sets the variable only if it is set on the host.

--help, -h

Show this help message and exit

--image=IMAGE

OCI container image to run with specified AI model. RamaLama defaults to using images based on the accelerator it discovers and the selected --backend. For example: quay.io/ramalama/ramalama. See the table below for all default images. The default image tag is based on the minor version of the RamaLama package. Version 0.18.0 of RamaLama pulls an image with a :0.18 tag from the quay.io/ramalama OCI repository. The --image option overrides this default.

The default can be overridden in the ramalama.conf file or via the RAMALAMA_IMAGE environment variable. export RAMALAMA_IMAGE=quay.io/ramalama/aiimage:1.2 tells RamaLama to use the quay.io/ramalama/aiimage:1.2 image.

Note: The --backend option provides a higher-level way to select the appropriate image based on GPU type. Use --backend to select vulkan, rocm, cuda, sycl, or openvino backends, which will automatically choose the correct image. Use --image only when you need to override the image selection entirely.

Accelerated images:

Backend / Accelerator	Image
CPU, Vulkan	quay.io/ramalama/ramalama
ROCm (AMD)	quay.io/ramalama/rocm
CUDA (NVIDIA)	quay.io/ramalama/cuda
Intel GPU (sycl)	quay.io/ramalama/intel-gpu
Intel GPU (openvino)	quay.io/ramalama/openvino
Asahi (Apple Silicon)	quay.io/ramalama/asahi
CANN (Ascend)	quay.io/ramalama/cann
MUSA (Moore Threads)	quay.io/ramalama/musa

Upstream llama.cpp "full" images from ghcr.io/ggml-org/llama.cpp are also supported. RamaLama automatically detects the image type and adjusts the container CLI accordingly.

ramalama run --image ghcr.io/ggml-org/llama.cpp:full-vulkan MODEL

--interactive, -i

Continue to interactive chat mode after processing stdin or prompt arguments. By default, when arguments or piped input are provided, the command exits after displaying the response. This flag allows you to continue chatting interactively.

--keep-groups

pass --group-add keep-groups to podman (default: False) If GPU device on host system is accessible to user via group access, this option leaks the groups into the container.

--keepalive

duration to keep a model loaded (e.g. 5m)

--logfile=path

Log output to a file

--max-tokens=integer

Maximum number of tokens to generate. Set to 0 for unlimited output (default: 0). This parameter is mapped to the appropriate runtime-specific parameter:

• llama.cpp: -n parameter
• MLX: --max-tokens parameter
• vLLM: --max-model-len parameter (mapped via ctx_size)

--mcp=SERVER_URL

MCP (Model Context Protocol) servers to use for enhanced tool-calling capabilities. This option can be specified multiple times to connect to multiple MCP servers. Each server provides tools that can be automatically invoked during chat conversations.

--model-draft

A draft model is a smaller, faster model that helps accelerate the decoding process of larger, more complex models, like Large Language Models (LLMs). It works by generating candidate sequences of tokens that the larger model then verifies and refines. This approach, often referred to as speculative decoding, can significantly improve the speed of inferencing by reducing the number of times the larger model needs to be invoked.

Use --runtime-args to pass the other draft model related parameters. Make sure the sampling parameters like top_k on the web UI are set correctly.

--name, -n

Name of the container to run the Model in.

--network=none

set the network mode for the container

--ngl

Number of layers to store in VRAM: a number, auto, or all. When omitted, llama-server defaults to auto.

--oci-runtime

Override the default OCI runtime used to launch the container. Container engines like Podman and Docker, have their own default oci runtime that they use. Using this option RamaLama will override these defaults.

On Nvidia based GPU systems, RamaLama defaults to using the nvidia-container-runtime. Use this option to override this selection.

--port, -p=port

Port for AI Model server to listen on (default: 8080)

The default can be overridden in the ramalama.conf file.

--prefix

Prefix for the user prompt (default: 🦭 > )

--privileged

By default, RamaLama containers are unprivileged (=false) and cannot, for example, modify parts of the operating system. This is because by de‐ fault a container is only allowed limited access to devices. A "privi‐ leged" container is given the same access to devices as the user launch‐ ing the container, with the exception of virtual consoles (/dev/tty\d+) when running in systemd mode (--systemd=always).

A privileged container turns off the security features that isolate the container from the host. Dropped Capabilities, limited devices, read- only mount points, Apparmor/SELinux separation, and Seccomp filters are all disabled. Due to the disabled security features, the privileged field should almost never be set as containers can easily break out of confinement.

Containers running in a user namespace (e.g., rootless containers) can‐ not have more privileges than the user that launched them.

--pull=policy

Pull image policy. The default is missing.

• always: Always pull the image and throw an error if the pull fails.
• missing: Only pull the image when it does not exist in the local containers storage. Throw an error if no image is found and the pull fails.
• never: Never pull the image but use the one from the local containers storage. Throw an error when no image is found.
• newer: Pull if the image on the registry is newer than the one in the local containers storage. An image is considered to be newer when the digests are different. Comparing the time stamps is prone to errors. Pull errors are suppressed if a local image was found.

--rag=

Specify path to Retrieval-Augmented Generation (RAG) database or an OCI Image containing a RAG database

note

RAG support requires AI Models be run within containers, --nocontainer not supported. Docker does not support image mounting, meaning Podman support required.

--rag-image=

The image to use to process the RAG database specified by the --rag option. The image must contain the /usr/bin/rag_framework executable, which will create a proxy which embellishes client requests with RAG data before passing them on to the LLM, and returns the responses.

--runtime-args="args"

Add args to the runtime (llama.cpp or vllm) invocation.

--seed=

Specify a seed rather than using a random seed for model interaction.

--selinux=true

Enable SELinux container separation (default: true)

--summarize-after=N

Automatically summarize conversation history after N messages to prevent context growth. When enabled, ramalama will periodically condense older messages into a summary, keeping only recent messages and the summary. This prevents the context from growing indefinitely during long chat sessions. Set to 0 to disable (default: 4).

--temp="0.8"

Temperature of the response from the AI Model. llama.cpp explains this as:

The lower the number is, the more deterministic the response.

The higher the number is the more creative the response is, but more likely to hallucinate when set too high.

Usage: Lower numbers are good for virtual assistants where we need deterministic responses. Higher numbers are good for roleplay or creative tasks like editing stories.

--thinking=BOOL

Enable or disable thinking mode in reasoning models. Maps to --reasoning on or --reasoning off in llama-server. When omitted, llama-server defaults to auto.

--threads, -t

Maximum number of cpu threads to use. The default is to use half the cores when more than 4 cores are available; otherwise, the default is 4 threads.

--tls-verify=true

Require HTTPS and verify certificates when contacting OCI registries

Description

Run specified AI Model as a chat bot. RamaLama pulls specified AI Model from registry if it does not exist in local storage. By default a prompt for a chat bot is started. When arguments or stdin are provided, they will be given to the AI Model and the output is returned. By default, the command exits after displaying the response, but you can use --interactive (-i) to continue to an interactive chat session after processing the initial prompt.

INTERACTIVE COMMANDS

When running in interactive chat mode, including after an initial prompt with --interactive, the following commands are available. All commands are case-insensitive (e.g., /CLEAR, /Clear, and /clear all work).

/help, help, ?

Display help information showing all available commands and their descriptions.

/clear

Clear the conversation history without exiting the chat session. This resets the context and allows starting a fresh conversation without restarting the container or connection. A confirmation message will be displayed when the history is cleared.

/bye, exit

Exit the chat session and close the connection.

/tool [question]

(Only available when using --mcp) Manually select which MCP tool to use for a question. Without this command, the AI automatically decides whether to use tools based on the question.

Ctrl + D

Exit the chat session (EOF signal).

Examples

Run command without arguments starts a chatbot

ramalama run granite
>

Run command with local downloaded model for 10 minutes

ramalama run --keepalive 10m file:///tmp/mymodel
>

Run command with a custom port to allow multiple models running simultaneously

ramalama run --port 8081 granite
>

Send an initial prompt via stdin and continue chatting interactively

$ echo "Explain quantum computing" | ramalama run -i granite
[AI response...]
> Can you give me an example?
[AI response...]
> /bye

ramalama run merlinite "when is the summer solstice"
The summer solstice, which is the longest day of the year, will happen on June ...

Run command with a custom prompt and a file passed by the stdin

cat file.py | ramalama run quay.io/USER/granite-code:1.0 'what does this program do?'

This program is a Python script that allows the user to interact with a terminal. ...
 [end of text]

Run command and send multiple lines at once to the chatbot by adding a backslash \ at the end of the line

$ ramalama run granite
🦭 > Hi \
🦭 > tell me a funny story \
🦭 > please

Clear conversation history during a chat session

$ ramalama run granite
🦭 > What is the capital of France?
Paris
🦭 > /clear
Conversation history cleared.
🦭 > What is 2+2?
4

Exit Codes:

0 Success 124 RamaLama command did not exit within the keepalive time.

NVIDIA CUDA Support

See ramalama-cuda(7) for setting up the host Linux system for CUDA support.

See Also

ramalama(1), ramalama-cuda(7), ramalama.conf(5)

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Aug 2024, Originally compiled by Dan Walsh <dwalsh@redhat.com>