Introduction

The Claude 3 family introduces a new generation of large multimodal models, including Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku. These models marks a significant milestone in the evolution of large multimodal models, introducing a suite of capabilities that redefine the benchmarks across a variety of domains including reasoning, math, coding, multilingual understanding, and vision quality.

Claude 3 Sonnet Model

Claude 3 Sonnet is part of the broader Claude 3 family, which also includes Claude 3 Opus and Claude 3 Haiku. Sonnet is designed to provide a middle ground in terms of performance and speed, making it an ideal choice for applications requiring both efficiency and a high level of capability. Like its siblings, Sonnet is a multimodal model, capable of processing and analyzing both text and image data.

Claude 3 Sonnet has a ability to perform nuanced content creation, accurate summarization, and handle complex scientific queries. The model demonstrates increased proficiency in non-English languages and coding tasks, supporting a wider range of use cases on a global scale.

Run Claude-3 Sonnet with an API

Running the API with Clarifai's Python SDK

You can run the Claude-3 Sonnet Model API using Clarifai’s Python SDK.

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

Predict via Image URL

from clarifai.client.model import Model
from clarifai.client.input import Inputs

prompt = "What time of day is it?"
image_url = "https://samples.clarifai.com/metro-north.jpg"

inference_params = dict(temperature=0.2, max_tokens=100, top_k=50, system_prompt="You're the helpful assistent")

model_prediction = Model("https://clarifai.com/anthropic/completion/models/claude-3-sonnet").predict(inputs = [Inputs.get_multimodal_input(input_id="",image_url=image_url, raw_text=prompt)],inference_params=inference_params)

print(model_prediction.outputs[0].data.text.raw)

You can also run Claude-3 Sonnet API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.

Predict via local Image

from clarifai.client.model import Model
from clarifai.client.input import Inputs

IMAGE_FILE_LOCATION = 'LOCAL IMAGE PATH'
with open(IMAGE_FILE_LOCATION, "rb") as f:
file_bytes = f.read()

prompt = "What time of day is it?"
inference_params = dict(temperature=0.2, max_tokens=100, top_k=50, system_prompt="You're the helpful assistent")

model_prediction = Model("https://clarifai.com/anthropic/completion/models/claude-3-sonnet").predict(inputs = [Inputs.get_multimodal_input(input_id="", image_bytes = file_bytes, raw_text=prompt)], inference_params=inference_params)
print(model_prediction.outputs[0].data.text.raw)

Note

Providing an image as input is optional. The model will function even if no image is provided.

Aliases: claude-3, claude 3, claude3 Sonnet, claude 3 sonnet

Use Cases

Claude 3 Sonnet excels in a wide range of tasks including but not limited to:

• Open-ended conversation and collaboration on ideas.
• Coding tasks and text-related operations such as searching, writing, editing, outlining, and summarizing.
• Interpreting visual inputs like charts, graphs, and photos to support productivity and decision-making processes.
• Specialized applications requiring nuanced content creation, analysis, forecasting, and accurate summarization.

Dataset

Claude 3 models were trained on a proprietary mix of publicly available information and non-public data up to August 2023, employing advanced data cleaning and filtering methods.

Advantages

• Multimodal Capabilities: Claude 3 Sonnet's ability to process both text and image data opens up a plethora of new applications and use cases.
• Language Proficiency: Improved fluency in non-English languages enhances its global applicability.
• Balance of Speed and Quality: It offers a compelling combination of high-quality outputs with efficiency, making it suitable for a wide range of applications.

Limitations

While Claude's capabilities are cutting-edge, there are some limitations to be aware of:

• High-Stakes Use Cases: As with other models of its kind, Claude 3 Sonnet should not be used as a standalone solution in situations where errors could lead to significant harm.
• Current Knowledge Limit: The model's knowledge is current up to mid-2023, and it does not have real-time web search capabilities.
• Language Performance Variance: While it has improved multilingual capabilities, performance may vary across languages, especially those with fewer resources.