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Selecting Models

Dria Network is a network of LLMs. When a task is published to the network, you can specify which models you want to assign your task to.

See models

Model enum provides a list of models that you can use in your tasks.

from dria import Model

DatasetGenerator.generate() has modelsparam to assign models to your task.

Following task will be executed by LLAMA3_1_8B_FP16 model. If the model is not available within network, SDK will poll the network until it finds an available LLAMA3_1_8B_FP16 model. 

DatasetGenerator(dataset=dataset).generate(
    instructions=instructions, singletons=prompter, models=Model.LLAMA3_1_8B_FP16
)

Model Availability?

Dria Network consists of multiple nodes, each running one or more available models. When a task is published, nodes with the selected model execute the task asynchronously.

For example, if the network has 100 Llama3.2-3B models, publishing a task with the Llama3.2-3B model will be handled by one of those models. Publishing 100 tasks will distribute each to one of the 100 available models. However, if you publish a 101st task, task will wait in queue until a Llama3.2-3B model becomes available.

Singe Task, Multiple Models

Dria SDK enables you to publish a single task to multiple models. This is useful when you want to compare the results of different models on the same task. Following example uses same instruction with different available open-source LLM and returns the results.

instructions = [{"topic": "Decentralized synthetic data"}, {"topic": "Decentralized synthetic data"}]

DatasetGenerator(dataset=dataset).generate(
    instructions=instructions, singletons=prompter, models=[Model.LLAMA3_1_8B_FP16, Model.LLAMA_3_1_70B_OR]
)

You can also select providers as your models. 

# Providers
OLLAMA = "ollama"  # Open source models
OPENAI = "openai"  # OpenAI models
GEMINI = "gemini"  # Gemini models
OPENROUTER = "openrouter"  # OpenRouter models
CODER = "coder"  # Coder models

Also models can selected by their sizes. 

# By sizes
SMALL = "small"  # Small-size models
MID = "mid"  # Mid-size models
LARGE = "large"  # Large-size models