5.3 KiB
API
Endpoints
- Generate a completion
- Create a model
- List local models
- Copy a model
- Delete a model
- Pull a model
- Generate embeddings
Conventions
Model names
Model names follow a model:tag
format. Some examples are orca: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.
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 nameprompt
: the prompt to generate a response for
Advanced parameters:
options
: additional model parameters listed in the documentation for the Modelfile such astemperature
system
: system prompt to (overrides what is defined in theModelfile
)template
: the full prompt or prompt template (overrides what is defined in theModelfile
)context
: the context parameter returned from a previous request to/generate
, this can be used to keep a short conversational memory
Request
curl -X POST http://localhost:11434/api/generate -d '{
"model": "llama2:7b",
"prompt": "Why is the sky blue?"
}'
Response
A stream of JSON objects:
{
"model": "llama2:7b",
"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 responseload_duration
: time spent in nanoseconds loading the modelsample_count
: number of samples generatedsample_duration
: time spent generating samplesprompt_eval_count
: number of tokens in the promptprompt_eval_duration
: time spent in nanoseconds evaluating the prompteval_count
: number of tokens the responseeval_duration
: time in nanoseconds spent generating the responsecontext
: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
To calculate how fast the response is generated in tokens per second (token/s), divide eval_count
/ eval_duration
.
{
"model": "llama2:7b",
"created_at": "2023-08-04T19:22:45.499127Z",
"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
}
Create a Model
POST /api/create
Create a model from a Modelfile
Parameters
name
: name of the model to createpath
: path to the Modelfile
Request
curl -X POST http://localhost:11434/api/create -d '{
"name": "mario",
"path": "~/Modelfile"
}'
Response
A stream of JSON objects. When finished, status
is success
{
"status": "parsing modelfile"
}
List Local Models
GET /api/tags
List models that are available locally.
Request
curl http://localhost:11434/api/tags
Response
{
"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
}
]
}
Copy a Model
POST /api/copy
Copy a model. Creates a model with another name from an existing model.
Request
curl http://localhost:11434/api/copy -d '{
"source": "llama2:7b",
"destination": "llama2-backup"
}'
Delete a Model
DELETE /api/delete
Delete a model and its data.
Parameters
model
: model name to delete
Request
curl -X DELETE http://localhost:11434/api/delete -d '{
"name": "llama2:13b"
}'
Pull a Model
POST /api/pull
Download a model from a the model registry. Cancelled pulls are resumed from where they left off, and multiple calls to will share the same download progress.
Parameters
name
: name of the model to pull
Request
curl -X POST http://localhost:11434/api/pull -d '{
"name": "llama2:7b"
}'
Response
{
"status": "downloading digestname",
"digest": "digestname",
"total": 2142590208
}
Generate Embeddings
POST /api/embeddings
Generate embeddings from a model
Parameters
model
: name of model to generate embeddings fromprompt
: text to generate embeddings for
Request
curl -X POST http://localhost:11434/api/embeddings -d '{
"model": "llama2:7b",
"prompt": "Here is an article about llamas..."
}'
Response
{
"embeddings": [
0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
]
}