Swarm Models - Pytorch
Project description
Swarm Models
Swarm Models provides a unified, secure, and highly scalable interface for interacting with multiple LLM and multi-modal APIs across different providers. It is built to streamline your API integrations, ensuring production-grade reliability and robust performance.
Key Features:
-
Multi-Provider Support: Integrate seamlessly with APIs from OpenAI, Anthropic, Azure, and more.
-
Enterprise-Grade Security: Built-in security protocols to protect your API keys and sensitive data, ensuring compliance with industry standards.
-
Lightning-Fast Performance: Optimized for low-latency and high-throughput, Swarm Models delivers blazing-fast API responses, suitable for real-time applications.
-
Ease of Use: Simplified API interaction with intuitive
.run(task)
and__call__
methods, making integration effortless. -
Scalability for All Use Cases: Whether it's a small script or a massive enterprise-scale application, Swarm Models scales effortlessly.
-
Production-Grade Reliability: Tested and proven in enterprise environments, ensuring consistent uptime and failover capabilities.
Onboarding
Swarm Models simplifies the way you interact with different APIs by providing a unified interface for all models.
1. Install Swarm Models
$ pip3 install -U swarm-models
2. Set Your Keys
OPENAI_API_KEY="your_openai_api_key"
GROQ_API_KEY="your_groq_api_key"
ANTHROPIC_API_KEY="your_anthropic_api_key"
AZURE_OPENAI_API_KEY="your_azure_openai_api_key"
3. Initialize a Model
Import the desired model from the package and initialize it with your API key or necessary configuration.
from swarm_models import YourDesiredModel
model = YourDesiredModel(api_key='your_api_key', *args, **kwargs)
4. Run Your Task
Use the .run(task)
method or simply call the model like model(task)
with your task.
task = "Define your task here"
result = model.run(task)
# Or equivalently
#result = model(task)
5. Enjoy the Results
print(result)
Full Code Example
from swarm_models import OpenAIChat
import os
# Get the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")
# Create an instance of the OpenAIChat class
model = OpenAIChat(openai_api_key=api_key, model_name="gpt-4o-mini")
# Query the model with a question
out = model(
"What is the best state to register a business in the US for the least amount of taxes?"
)
# Print the model's response
print(out)
TogetherLLM
Documentation
The TogetherLLM
class is designed to simplify the interaction with Together's LLM models. It provides a straightforward way to run tasks on these models, including support for concurrent and batch processing.
Initialization
To use TogetherLLM
, you need to initialize it with your API key, the name of the model you want to use, and optionally, a system prompt. The system prompt is used to provide context to the model for the tasks you will run.
Here's an example of how to initialize TogetherLLM
:
import os
from swarm_models import TogetherLLM
model_runner = TogetherLLM(
api_key=os.environ.get("TOGETHER_API_KEY"),
model_name="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
system_prompt="You're Larry fink",
)
Running Tasks
Once initialized, you can run tasks on the model using the run
method. This method takes a task string as an argument and returns the response from the model.
Here's an example of running a single task:
task = "How do we allocate capital efficiently in your opinion Larry?"
response = model_runner.run(task)
print(response)
Running Multiple Tasks Concurrently
TogetherLLM
also supports running multiple tasks concurrently using the run_concurrently
method. This method takes a list of task strings and returns a list of responses from the model.
Here's an example of running multiple tasks concurrently:
tasks = [
"What are the top-performing mutual funds in the last quarter?",
"How do I evaluate the risk of a mutual fund?",
"What are the fees associated with investing in a mutual fund?",
"Can you recommend a mutual fund for a beginner investor?",
"How do I diversify my portfolio with mutual funds?",
]
responses = model_runner.run_concurrently(tasks)
for response in responses:
print(response)
Enterprise-Grade Features
-
Security: API keys and user data are handled with utmost care, utilizing encryption and best security practices to protect your sensitive information.
-
Production Reliability: Swarm Models has undergone rigorous testing to ensure that it can handle high traffic and remains resilient in enterprise-grade environments.
-
Fail-Safe Mechanisms: Built-in failover handling to ensure uninterrupted service even under heavy load or network issues.
-
Unified API: No more dealing with multiple SDKs or libraries. Swarm Models standardizes your interactions across providers like OpenAI, Anthropic, Azure, and more, so you can focus on what matters.
Available Models
Model Name | Description |
---|---|
OpenAIChat |
Chat model for OpenAI's GPT-3 and GPT-4 APIs. |
Anthropic |
Model for interacting with Anthropic's APIs. |
AzureOpenAI |
Azure's implementation of OpenAI's models. |
Dalle3 |
Model for generating images from text prompts. |
NvidiaLlama31B |
Llama model for causal language generation. |
Fuyu |
Multi-modal model for image and text processing. |
Gemini |
Multi-modal model for vision and language tasks. |
Vilt |
Vision-and-Language Transformer for question answering. |
TogetherLLM |
Model for collaborative language tasks. |
FireWorksAI |
Model for generating creative content. |
ReplicateChat |
Chat model for replicating conversations. |
HuggingfaceLLM |
Interface for Hugging Face models. |
CogVLMMultiModal |
Multi-modal model for vision and language tasks. |
LayoutLMDocumentQA |
Model for document question answering. |
GPT4VisionAPI |
Model for analyzing images with GPT-4 capabilities. |
LlamaForCausalLM |
Causal language model from the Llama family. |
GroundedSAMTwo |
Analyzes and track objects in images. GPU Only |
Support & Contributions
- Documentation: Comprehensive guides, API references, and best practices are available in our official Documentation.
- GitHub: Explore the code, report issues, and contribute to the project via our GitHub repository.
License
Swarm Models is released under the MIT License.
Todo
- Add cohere models command r
- Add gemini and google ai studio
- Integrate ollama extensively
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