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% ramalama-serve 1

NAME

ramalama-serve - serve REST API on specified AI Model

SYNOPSIS

ramalama serve [options] model

DESCRIPTION

Serve specified AI Model as a chat bot. RamaLama pulls specified AI Model from registry if it does not exist in local storage.

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
Ollama ollama:// ollama.com
OCI Container Registries oci:// opencontainers.org
Examples: quay.io, Docker Hub,Artifactory

RamaLama defaults to the Ollama registry transport. This default can be overridden in the ramalama.conf file or via the RAMALAMA_TRANSPORTS environment. export RAMALAMA_TRANSPORT=huggingface Changes RamaLama to use huggingface transport.

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

REST API ENDPOINTS

Under the hood, ramalama-serve uses the LLaMA.cpp HTTP server by default.

For REST API endpoint documentation, see: https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md#api-endpoints

OPTIONS

--authfile=password

path of the authentication file for OCI registries

--ctx-size, -c

size of the prompt context (default: 2048, 0 = loaded from model)

--detach, -d

Run the container in the background and print the new container ID. The default is TRUE. The --nocontainer option forces this option to False.

Use the ramalama stop command to stop the container running the served ramalama 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.

--generate=type

Generate specified configuration format for running the AI Model as a service

Key Description
quadlet Podman supported container definition for running AI Model under systemd
kube Kubernetes YAML definition for running the AI Model as a service
quadlet/kube Kubernetes YAML definition for running the AI Model as a service and Podman supported container definition for running the Kube YAML specified pod under systemd

--help, -h

show this help message and exit

--host="0.0.0.0"

IP address for llama.cpp to listen on.

--name, -n

Name of the container to run the Model in.

--network=""

set the network mode for the container

--ngl

number of gpu layers, 0 means CPU inferencing, 999 means use max layers (default: -1) The default -1, means use whatever is automatically deemed appropriate (0 or 999)

--port, -p

port for AI Model server to listen on

--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.

--seed=

Specify seed rather than using random seed model interaction

--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

--tls-verify=true

require HTTPS and verify certificates when contacting OCI registries

EXAMPLES

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

Generate quadlet service off of HuggingFace granite Model

$ ramalama serve --name MyGraniteServer --generate=quadlet granite
Generating quadlet file: MyGraniteServer.container

$ cat MyGraniteServer.container
[Unit]
Description=RamaLama $HOME/.local/share/ramalama/models/huggingface/instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf AI Model Service
After=local-fs.target

[Container]
AddDevice=-/dev/dri
AddDevice=-/dev/kfd
Exec=llama-server --port 1234 -m $HOME/.local/share/ramalama/models/huggingface/instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf
Image=quay.io/ramalama/ramalama:latest
Mount=type=bind,src=/home/dwalsh/.local/share/ramalama/models/huggingface/instructlab/granite-7b-lab-GGUF/granite-7b-lab-Q4_K_M.gguf,target=/mnt/models/model.file,ro,Z
ContainerName=MyGraniteServer
PublishPort=8080

[Install]
# Start by default on boot
WantedBy=multi-user.target default.target

$ mv  MyGraniteServer.container $HOME/.config/containers/systemd/
$ systemctl --user daemon-reload
$ systemctl start --user MyGraniteServer
$ systemctl status --user MyGraniteServer
● MyGraniteServer.service - RamaLama granite AI Model Service
     Loaded: loaded (/home/dwalsh/.config/containers/systemd/MyGraniteServer.container; generated)
    Drop-In: /usr/lib/systemd/user/service.d
	    └─10-timeout-abort.conf
     Active: active (running) since Fri 2024-09-27 06:54:17 EDT; 3min 3s ago
   Main PID: 3706287 (conmon)
      Tasks: 20 (limit: 76808)
     Memory: 1.0G (peak: 1.0G)

...
$ podman ps
CONTAINER ID  IMAGE                             COMMAND               CREATED        STATUS        PORTS                    NAMES
7bb35b97a0fe  quay.io/ramalama/ramalama:latest  llama-server --po...  3 minutes ago  Up 3 minutes  0.0.0.0:43869->8080/tcp  MyGraniteServer

Generate quadlet service off of tiny OCI Model

$ ramalama --runtime=vllm serve --name tiny --generate=quadlet oci://quay.io/rhatdan/tiny:latest
Downloading quay.io/rhatdan/tiny:latest...
Trying to pull quay.io/rhatdan/tiny:latest...
Getting image source signatures
Copying blob 65ba8d40e14a skipped: already exists
Copying blob e942a1bf9187 skipped: already exists
Copying config d8e0b28ee6 done   |
Writing manifest to image destination
Generating quadlet file: tiny.container
Generating quadlet file: tiny.image
Generating quadlet file: tiny.volume

$cat tiny.container
[Unit]
Description=RamaLama /run/model/model.file AI Model Service
After=local-fs.target

[Container]
AddDevice=-/dev/dri
AddDevice=-/dev/kfd
Exec=vllm serve --port 8080 /run/model/model.file
Image=quay.io/ramalama/ramalama:latest
Mount=type=volume,source=tiny:latest.volume,dest=/mnt/models,ro
ContainerName=tiny
PublishPort=8080

[Install]
# Start by default on boot
WantedBy=multi-user.target default.target

$ cat tiny.volume
[Volume]
Driver=image
Image=tiny:latest.image

$ cat tiny.image
[Image]
Image=quay.io/rhatdan/tiny:latest

Generate a kubernetes YAML file named MyTinyModel

$ ramalama serve --name MyTinyModel --generate=kube oci://quay.io/rhatdan/tiny-car:latest
Generating Kubernetes YAML file: MyTinyModel.yaml
$ cat MyTinyModel.yaml
# Save the output of this file and use kubectl create -f to import
# it into Kubernetes.
#
# Created with ramalama-0.0.21
apiVersion: v1
kind: Deployment
metadata:
  name: MyTinyModel
  labels:
    app: MyTinyModel
spec:
  replicas: 1
  selector:
    matchLabels:
      app: MyTinyModel
  template:
    metadata:
      labels:
	app: MyTinyModel
    spec:
      containers:
      - name: MyTinyModel
	image: quay.io/ramalama/ramalama:latest
	command: ["llama-server"]
	args: ['--port', '8080', '-m', '/mnt/models/model.file']
	ports:
	- containerPort: 8080
	volumeMounts:
	- mountPath: /mnt/models
	  subPath: /models
	  name: model
	- mountPath: /dev/dri
	  name: dri
      volumes:
      - image:
	  reference: quay.io/rhatdan/tiny-car:latest
	  pullPolicy: IfNotPresent
	name: model
      - hostPath:
	  path: /dev/dri
	name: dri

Generate a kubernetes YAML file named MyTinyModel shown above, but also generate a quadlet to run it in.

$ ramalama --name MyTinyModel --generate=quadlet/kube oci://quay.io/rhatdan/tiny-car:latest
run_cmd:  podman image inspect quay.io/rhatdan/tiny-car:latest
Generating Kubernetes YAML file: MyTinyModel.yaml
Generating quadlet file: MyTinyModel.kube
$ cat MyTinyModel.kube
[Unit]
Description=RamaLama quay.io/rhatdan/tiny-car:latest Kubernetes YAML - AI Model Service
After=local-fs.target

[Kube]
Yaml=MyTinyModel.yaml

[Install]
# Start by default on boot
WantedBy=multi-user.target default.target

NVIDIA CUDA Support

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

SEE ALSO

ramalama(1), ramalama-stop(1), quadlet(1), systemctl(1), podman-ps(1), ramalama-cuda(7)

HISTORY

Aug 2024, Originally compiled by Dan Walsh [email protected]