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ollama/docs/api.md
Michael Yang 54f92f01cb update docs
2023-11-15 15:28:15 -08:00

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API

Endpoints

Conventions

Model names

Model names follow a model:tag format. Some examples are orca-mini:3b-q4_1 and llama2:70b. The tag is optional and, if not provided, will default to latest. The tag is used to identify a specific version.

Durations

All durations are returned in nanoseconds.

Streaming responses

Certain endpoints stream responses as JSON objects delineated with the newline (\n) character.

Generate a completion

POST /api/generate

Generate a response for a given prompt with a provided model. This is a streaming endpoint, so will be a series of responses. The final response object will include statistics and additional data from the request.

Parameters

  • model: (required) the model name
  • prompt: the prompt to generate a response for

Advanced parameters (optional):

  • format: the format to return a response in. Currently the only accepted value is json
  • options: additional model parameters listed in the documentation for the Modelfile such as temperature
  • system: system prompt to (overrides what is defined in the Modelfile)
  • template: the full prompt or prompt template (overrides what is defined in the Modelfile)
  • context: the context parameter returned from a previous request to /generate, this can be used to keep a short conversational memory
  • stream: if false the response will be returned as a single response object, rather than a stream of objects
  • raw: if true no formatting will be applied to the prompt and no context will be returned. You may choose to use the raw parameter if you are specifying a full templated prompt in your request to the API, and are managing history yourself.

JSON mode

Enable JSON mode by setting the format parameter to json and specifying the model should use JSON in the prompt. This will structure the response as valid JSON. See the JSON mode example below.

Examples

Request

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt": "Why is the sky blue?"
}'

Response

A stream of JSON objects is returned:

{
  "model": "llama2",
  "created_at": "2023-08-04T08:52:19.385406455-07:00",
  "response": "The",
  "done": false
}

The final response in the stream also includes additional data about the generation:

  • total_duration: time spent generating the response
  • load_duration: time spent in nanoseconds loading the model
  • sample_count: number of samples generated
  • sample_duration: time spent generating samples
  • prompt_eval_count: number of tokens in the prompt
  • prompt_eval_duration: time spent in nanoseconds evaluating the prompt
  • eval_count: number of tokens the response
  • eval_duration: time in nanoseconds spent generating the response
  • context: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
  • response: empty if the response was streamed, if not streamed, this will contain the full response

To calculate how fast the response is generated in tokens per second (token/s), divide eval_count / eval_duration.

{
  "model": "llama2",
  "created_at": "2023-08-04T19:22:45.499127Z",
  "response": "",
  "context": [1, 2, 3],
  "done": true,
  "total_duration": 5589157167,
  "load_duration": 3013701500,
  "sample_count": 114,
  "sample_duration": 81442000,
  "prompt_eval_count": 46,
  "prompt_eval_duration": 1160282000,
  "eval_count": 113,
  "eval_duration": 1325948000
}

Request (No streaming)

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "llama2:7b",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

Response

If stream is set to false, the response will be a single JSON object:

{
  "model": "llama2:7b",
  "created_at": "2023-08-04T19:22:45.499127Z",
  "response": "The sky is blue because it is the color of the sky.",
  "context": [1, 2, 3],
  "done": true,
  "total_duration": 5589157167,
  "load_duration": 3013701500,
  "sample_count": 114,
  "sample_duration": 81442000,
  "prompt_eval_count": 46,
  "prompt_eval_duration": 1160282000,
  "eval_count": 13,
  "eval_duration": 1325948000
}

Request (Raw mode)

In some cases you may wish to bypass the templating system and provide a full prompt. In this case, you can use the raw parameter to disable formatting and context.

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "mistral",
  "prompt": "[INST] why is the sky blue? [/INST]",
  "raw": true,
  "stream": false
}'

Response

{
  "model": "mistral",
  "created_at": "2023-11-03T15:36:02.583064Z",
  "response": " The sky appears blue because of a phenomenon called Rayleigh scattering.",
  "done": true,
  "total_duration": 14648695333,
  "load_duration": 3302671417,
  "prompt_eval_count": 14,
  "prompt_eval_duration": 286243000,
  "eval_count": 129,
  "eval_duration": 10931424000
}

Request (JSON mode)

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt": "What color is the sky at different times of the day? Respond using JSON",
  "format": "json",
  "stream": false
}'

Response

{
  "model": "llama2",
  "created_at": "2023-11-09T21:07:55.186497Z",
  "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
  "done": true,
  "total_duration": 4661289125,
  "load_duration": 1714434500,
  "prompt_eval_count": 36,
  "prompt_eval_duration": 264132000,
  "eval_count": 75,
  "eval_duration": 2112149000
}

The value of response will be a string containing JSON similar to:

{
  "morning": {
    "color": "blue"
  },
  "noon": {
    "color": "blue-gray"
  },
  "afternoon": {
    "color": "warm gray"
  },
  "evening": {
    "color": "orange"
  }
}

Request (With options)

If you want to set custom options for the model at runtime rather than in the Modelfile, you can do so with the options parameter. This example sets every available option, but you can set any of them individually and omit the ones you do not want to override.

curl -X POST http://localhost:11434/api/generate -d '{
  "model": "llama2:7b",
  "prompt": "Why is the sky blue?",
  "stream": false,
  "options": {
    "num_keep": 5,
    "seed": 42,
    "num_predict": 100,
    "top_k": 20,
    "top_p": 0.9,
    "tfs_z": 0.5,
    "typical_p": 0.7,
    "repeat_last_n": 33,
    "temperature": 0.8,
    "repeat_penalty": 1.2,
    "presence_penalty": 1.5,
    "frequency_penalty": 1.0,
    "mirostat": 1,
    "mirostat_tau": 0.8,
    "mirostat_eta": 0.6,
    "penalize_newline": true,
    "stop": ["\n", "user:"],
    "numa": false,
    "num_ctx": 4,
    "num_batch": 2,
    "num_gqa": 1,
    "num_gpu": 1,
    "main_gpu": 0,
    "low_vram": false,
    "f16_kv": true,
    "logits_all": false,
    "vocab_only": false,
    "use_mmap": true,
    "use_mlock": false,
    "embedding_only": false,
    "rope_frequency_base": 1.1,
    "rope_frequency_scale": 0.8,
    "num_thread": 8
    }
}'

Response

{
  "model": "llama2:7b",
  "created_at": "2023-08-04T19:22:45.499127Z",
  "response": "The sky is blue because it is the color of the sky.",
  "context": [1, 2, 3],
  "done": true,
  "total_duration": 5589157167,
  "load_duration": 3013701500,
  "sample_count": 114,
  "sample_duration": 81442000,
  "prompt_eval_count": 46,
  "prompt_eval_duration": 1160282000,
  "eval_count": 13,
  "eval_duration": 1325948000
}

Create a Model

POST /api/create

Create a model from a Modelfile. It is recommended to set modelfile to the content of the Modelfile rather than just set path. This is a requirement for remote create. Remote model creation should also create any file blobs, fields such as FROM and ADAPTER, explicitly with the server using Create a Blob and the value to the path indicated in the response.

Parameters

  • name: name of the model to create
  • path: path to the Modelfile (deprecated: please use modelfile instead)
  • modelfile: contents of the Modelfile
  • stream: (optional) if false the response will be returned as a single response object, rather than a stream of objects

Examples

Request

curl -X POST http://localhost:11434/api/create -d '{
  "name": "mario",
  "path": "~/Modelfile",
  "modelfile": "FROM llama2"
}'

Response

A stream of JSON objects. When finished, status is success.

{
  "status": "parsing modelfile"
}

Check if a Blob Exists

HEAD /api/blobs/:digest

Check if a blob is known to the server.

Query Parameters

  • digest: the SHA256 digest of the blob

Examples

Request
curl -I http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
Response

Return 200 OK if the blob exists, 404 Not Found if it does not.

Create a Blob

POST /api/blobs/:digest

Create a blob from a file. Returns the server file path.

Query Parameters

  • digest: the expected SHA256 digest of the file

Examples

Request
curl -T model.bin -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
Response

Return 201 Created if the blob was successfully created.

List Local Models

GET /api/tags

List models that are available locally.

Examples

Request

curl http://localhost:11434/api/tags

Response

A single JSON object will be returned.

{
  "models": [
    {
      "name": "llama2:7b",
      "modified_at": "2023-08-02T17:02:23.713454393-07:00",
      "size": 3791730596
    },
    {
      "name": "llama2:13b",
      "modified_at": "2023-08-08T12:08:38.093596297-07:00",
      "size": 7323310500
    }
  ]
}

Show Model Information

POST /api/show

Show details about a model including modelfile, template, parameters, license, and system prompt.

Parameters

  • name: name of the model to show

Examples

Request

curl http://localhost:11434/api/show -d '{
  "name": "llama2:7b"
}'

Response

{
  "license": "<contents of license block>",
  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llama2:latest\n\nFROM /Users/username/.ollama/models/blobs/sha256:8daa9615cce30c259a9555b1cc250d461d1bc69980a274b44d7eda0be78076d8\nTEMPLATE \"\"\"[INST] {{ if and .First .System }}<<SYS>>{{ .System }}<</SYS>>\n\n{{ end }}{{ .Prompt }} [/INST] \"\"\"\nSYSTEM \"\"\"\"\"\"\nPARAMETER stop [INST]\nPARAMETER stop [/INST]\nPARAMETER stop <<SYS>>\nPARAMETER stop <</SYS>>\n",
  "parameters": "stop                           [INST]\nstop                           [/INST]\nstop                           <<SYS>>\nstop                           <</SYS>>",
  "template": "[INST] {{ if and .First .System }}<<SYS>>{{ .System }}<</SYS>>\n\n{{ end }}{{ .Prompt }} [/INST] "
}

Copy a Model

POST /api/copy

Copy a model. Creates a model with another name from an existing model.

Examples

Request

curl http://localhost:11434/api/copy -d '{
  "source": "llama2:7b",
  "destination": "llama2-backup"
}'

Response

The only response is a 200 OK if successful.

Delete a Model

DELETE /api/delete

Delete a model and its data.

Parameters

  • name: model name to delete

Examples

Request

curl -X DELETE http://localhost:11434/api/delete -d '{
  "name": "llama2:13b"
}'

Response

If successful, the only response is a 200 OK.

Pull a Model

POST /api/pull

Download a model from the ollama library. Cancelled pulls are resumed from where they left off, and multiple calls will share the same download progress.

Parameters

  • name: name of the model to pull
  • insecure: (optional) allow insecure connections to the library. Only use this if you are pulling from your own library during development.
  • stream: (optional) if false the response will be returned as a single response object, rather than a stream of objects

Examples

Request

curl -X POST http://localhost:11434/api/pull -d '{
  "name": "llama2:7b"
}'

Response

If stream is not specified, or set to true, a stream of JSON objects is returned:

The first object is the manifest:

{
  "status": "pulling manifest"
}

Then there is a series of downloading responses. Until any of the download is completed, the completed key may not be included. The number of files to be downloaded depends on the number of layers specified in the manifest.

{
  "status": "downloading digestname",
  "digest": "digestname",
  "total": 2142590208,
  "completed": 241970
}

After all the files are downloaded, the final responses are:

{
    "status": "verifying sha256 digest"
}
{
    "status": "writing manifest"
}
{
    "status": "removing any unused layers"
}
{
    "status": "success"
}

if stream is set to false, then the response is a single JSON object:

{
  "status": "success"
}

Push a Model

POST /api/push

Upload a model to a model library. Requires registering for ollama.ai and adding a public key first.

Parameters

  • name: name of the model to push in the form of <namespace>/<model>:<tag>
  • insecure: (optional) allow insecure connections to the library. Only use this if you are pushing to your library during development.
  • stream: (optional) if false the response will be returned as a single response object, rather than a stream of objects

Examples

Request

curl -X POST http://localhost:11434/api/push -d '{
  "name": "mattw/pygmalion:latest"
}'

Response

If stream is not specified, or set to true, a stream of JSON objects is returned:

{ "status": "retrieving manifest" }

and then:

{
  "status": "starting upload",
  "digest": "sha256:bc07c81de745696fdf5afca05e065818a8149fb0c77266fb584d9b2cba3711ab",
  "total": 1928429856
}

Then there is a series of uploading responses:

{
  "status": "starting upload",
  "digest": "sha256:bc07c81de745696fdf5afca05e065818a8149fb0c77266fb584d9b2cba3711ab",
  "total": 1928429856
}

Finally, when the upload is complete:

{"status":"pushing manifest"}
{"status":"success"}

If stream is set to false, then the response is a single JSON object:

{ "status": "success" }

Generate Embeddings

POST /api/embeddings

Generate embeddings from a model

Parameters

  • model: name of model to generate embeddings from
  • prompt: text to generate embeddings for

Advanced parameters:

  • options: additional model parameters listed in the documentation for the Modelfile such as temperature

Examples

Request

curl -X POST http://localhost:11434/api/embeddings -d '{
  "model": "llama2:7b",
  "prompt": "Here is an article about llamas..."
}'

Response

{
  "embedding": [
    0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
    0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
  ]
}