If you haven’t yet trained a model on Replicate, we recommend you read one of the following guides.

• Fine-tune a language model
• Fine-tune an image model
• Fine-tune SDXL with your own images

Pricing

Trainings for this model run on Nvidia A100 (80GB) GPU hardware, which costs $0.0014 per second.

Create a training

pip install replicate

import replicate

training = replicate.trainings.create(
  version="yorickvp/llava-13b:b5f6212d032508382d61ff00469ddda3e32fd8a0e75dc39d8a4191bb742157fb",
  input={
    ...
  },
  destination=f"{username}/<destination-model-name>"
)

print(training)

curl -s -X POST \
-d '{"destination": "{username}/<destination-model-name>", "input": {...}}' \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  https://api.replicate.com/v1/models/yorickvp/llava-13b/versions/b5f6212d032508382d61ff00469ddda3e32fd8a0e75dc39d8a4191bb742157fb/trainings

{
  "id": "zz4ibbonubfz7carwiefibzgga",
  "version": "b5f6212d032508382d61ff00469ddda3e32fd8a0e75dc39d8a4191bb742157fb",
  "status": "starting",
  "input": {
    "data": "..."
  },
  "output": null,
  "error": null,
  "logs": null,
  "started_at": null,
  "created_at": "2023-03-28T21:47:58.566434Z",
  "completed_at": null
}

Note that before you can create a training, you’ll need to create a model and use its name as the value for the destination field.

You can finetune LLaVA with your own dataset, using LoRA techniques! Training data can be passed to cog train with the train_data parameter. Your training dataset should be a zip-file with the following structure:

• ./images/: A folder with training data images.
• ./data.json: A JSON file that links images to conversations. For details, see the dataset format instructions in the github repository.

Example code for training:

import replicate

training = replicate.trainings.create(
    version="yorickvp/llava-13b:[version_id]",
    input={
        "train_data": "https://my-domain/my-input-images.zip",
    },
    destination="my-name/my-model"
)
print(training)

You can find more information about finetuning image models in the Replicate docs. The tutorial on finetuning SDXL with your own images is a good starting point.