# Swarm Models
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
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**
```bash
$ pip3 install -U swarm-models
```
### **2. Set Your Keys**
```bash
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.
```python
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.
```python
task = "Define your task here"
result = model.run(task)
# Or equivalently
#result = model(task)
```
### **5. Enjoy the Results**
```python
print(result)
```
---
## **Full Code Example**
```python
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`:
```python
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:
```python
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:
```python
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**
1. **Security**: API keys and user data are handled with utmost care, utilizing encryption and best security practices to protect your sensitive information.
2. **Production Reliability**: Swarm Models has undergone rigorous testing to ensure that it can handle high traffic and remains resilient in enterprise-grade environments.
3. **Fail-Safe Mechanisms**: Built-in failover handling to ensure uninterrupted service even under heavy load or network issues.
4. **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 | Import Path |
|---------------------------|------------------------------------------------------|
| BaseLLM | `from swarm_models.base_llm import BaseLLM` |
| BaseMultiModalModel | `from swarm_models.base_multimodal_model import BaseMultiModalModel` |
| GPT4VisionAPI | `from swarm_models.gpt4_vision_api import GPT4VisionAPI` |
| HuggingfaceLLM | `from swarm_models.huggingface import HuggingfaceLLM` |
| LayoutLMDocumentQA | `from swarm_models.layoutlm_document_qa import LayoutLMDocumentQA` |
| llama3Hosted | `from swarm_models.llama3_hosted import llama3Hosted` |
| LavaMultiModal | `from swarm_models.llava import LavaMultiModal` |
| Nougat | `from swarm_models.nougat import Nougat` |
| OpenAIEmbeddings | `from swarm_models.openai_embeddings import OpenAIEmbeddings` |
| OpenAITTS | `from swarm_models.openai_tts import OpenAITTS` |
| GooglePalm | `from swarm_models.palm import GooglePalm as Palm` |
| Anthropic | `from swarm_models.popular_llms import Anthropic as Anthropic` |
| AzureOpenAI | `from swarm_models.popular_llms import AzureOpenAILLM as AzureOpenAI` |
| Cohere | `from swarm_models.popular_llms import CohereChat as Cohere` |
| OctoAIChat | `from swarm_models.popular_llms import OctoAIChat` |
| OpenAIChat | `from swarm_models.popular_llms import OpenAIChatLLM as OpenAIChat` |
| OpenAILLM | `from swarm_models.popular_llms import OpenAILLM as OpenAI` |
| Replicate | `from swarm_models.popular_llms import ReplicateChat as Replicate` |
| QwenVLMultiModal | `from swarm_models.qwen import QwenVLMultiModal` |
| FireWorksAI | `from swarm_models.popular_llms import FireWorksAI` |
| Vilt | `from swarm_models.vilt import Vilt` |
| TogetherLLM | `from swarm_models.together_llm import TogetherLLM` |
| LiteLLM | `from swarm_models.lite_llm_model import LiteLLM` |
| OpenAIFunctionCaller | `from swarm_models.openai_function_caller import OpenAIFunctionCaller` |
| OllamaModel | `from swarm_models.ollama_model import OllamaModel` |
| GroundedSAMTwo | `from swarm_models.sam_two import GroundedSAMTwo` |
---
## **Support & Contributions**
- **Documentation**: Comprehensive guides, API references, and best practices are available in our official [Documentation](https://docs.swarms.world).
- **GitHub**: Explore the code, report issues, and contribute to the project via our [GitHub repository](https://github.com/The-Swarm-Corporation/swarm-models).
---
## **License**
Swarm Models is released under the [MIT License](https://github.com/The-Swarm-Corporation/swarm-models/LICENSE).
---
# Todo
- [ ] Add cohere models command r
- [ ] Add gemini and google ai studio
- [ ] Integrate ollama extensively
Raw data
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"description": "\n# Swarm Models\n\n[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)\n\nSwarm 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.\n\n## **Key Features**:\n\n- **Multi-Provider Support**: Integrate seamlessly with APIs from OpenAI, Anthropic, Azure, and more.\n \n- **Enterprise-Grade Security**: Built-in security protocols to protect your API keys and sensitive data, ensuring compliance with industry standards.\n\n- **Lightning-Fast Performance**: Optimized for low-latency and high-throughput, Swarm Models delivers blazing-fast API responses, suitable for real-time applications.\n\n- **Ease of Use**: Simplified API interaction with intuitive `.run(task)` and `__call__` methods, making integration effortless.\n\n- **Scalability for All Use Cases**: Whether it's a small script or a massive enterprise-scale application, Swarm Models scales effortlessly.\n\n- **Production-Grade Reliability**: Tested and proven in enterprise environments, ensuring consistent uptime and failover capabilities.\n\n---\n\n\n## **Onboarding**\n\nSwarm Models simplifies the way you interact with different APIs by providing a unified interface for all models.\n\n### **1. Install Swarm Models**\n\n```bash\n$ pip3 install -U swarm-models\n```\n\n### **2. Set Your Keys**\n\n```bash\nOPENAI_API_KEY=\"your_openai_api_key\"\nGROQ_API_KEY=\"your_groq_api_key\"\nANTHROPIC_API_KEY=\"your_anthropic_api_key\"\nAZURE_OPENAI_API_KEY=\"your_azure_openai_api_key\"\n```\n\n### **3. Initialize a Model**\n\nImport the desired model from the package and initialize it with your API key or necessary configuration.\n\n```python\nfrom swarm_models import YourDesiredModel\n\nmodel = YourDesiredModel(api_key='your_api_key', *args, **kwargs)\n```\n\n### **4. Run Your Task**\n\nUse the `.run(task)` method or simply call the model like `model(task)` with your task.\n\n```python\ntask = \"Define your task here\"\nresult = model.run(task)\n\n# Or equivalently\n#result = model(task)\n```\n\n### **5. Enjoy the Results**\n\n```python\nprint(result)\n```\n\n---\n\n## **Full Code Example**\n\n```python\nfrom swarm_models import OpenAIChat\nimport os\n\n# Get the OpenAI API key from the environment variable\napi_key = os.getenv(\"OPENAI_API_KEY\")\n\n# Create an instance of the OpenAIChat class\nmodel = OpenAIChat(openai_api_key=api_key, model_name=\"gpt-4o-mini\")\n\n# Query the model with a question\nout = model(\n \"What is the best state to register a business in the US for the least amount of taxes?\"\n)\n\n# Print the model's response\nprint(out)\n```\n\n---\n\n## `TogetherLLM` Documentation\n\nThe `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.\n\n### Initialization\n\nTo 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.\n\nHere's an example of how to initialize `TogetherLLM`:\n```python\nimport os\nfrom swarm_models import TogetherLLM\n\nmodel_runner = TogetherLLM(\n api_key=os.environ.get(\"TOGETHER_API_KEY\"),\n model_name=\"meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\",\n system_prompt=\"You're Larry fink\",\n)\n```\n### Running Tasks\n\nOnce 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.\n\nHere's an example of running a single task:\n```python\ntask = \"How do we allocate capital efficiently in your opinion Larry?\"\nresponse = model_runner.run(task)\nprint(response)\n```\n### Running Multiple Tasks Concurrently\n\n`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.\n\nHere's an example of running multiple tasks concurrently:\n```python\ntasks = [\n \"What are the top-performing mutual funds in the last quarter?\",\n \"How do I evaluate the risk of a mutual fund?\",\n \"What are the fees associated with investing in a mutual fund?\",\n \"Can you recommend a mutual fund for a beginner investor?\",\n \"How do I diversify my portfolio with mutual funds?\",\n]\nresponses = model_runner.run_concurrently(tasks)\nfor response in responses:\n print(response)\n```\n\n\n## **Enterprise-Grade Features**\n\n1. **Security**: API keys and user data are handled with utmost care, utilizing encryption and best security practices to protect your sensitive information.\n \n2. **Production Reliability**: Swarm Models has undergone rigorous testing to ensure that it can handle high traffic and remains resilient in enterprise-grade environments.\n\n3. **Fail-Safe Mechanisms**: Built-in failover handling to ensure uninterrupted service even under heavy load or network issues.\n\n4. **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.\n\n---\n\n## **Available Models**\n\n| Model Name | Import Path |\n|---------------------------|------------------------------------------------------|\n| BaseLLM | `from swarm_models.base_llm import BaseLLM` |\n| BaseMultiModalModel | `from swarm_models.base_multimodal_model import BaseMultiModalModel` |\n| GPT4VisionAPI | `from swarm_models.gpt4_vision_api import GPT4VisionAPI` |\n| HuggingfaceLLM | `from swarm_models.huggingface import HuggingfaceLLM` |\n| LayoutLMDocumentQA | `from swarm_models.layoutlm_document_qa import LayoutLMDocumentQA` |\n| llama3Hosted | `from swarm_models.llama3_hosted import llama3Hosted` |\n| LavaMultiModal | `from swarm_models.llava import LavaMultiModal` |\n| Nougat | `from swarm_models.nougat import Nougat` |\n| OpenAIEmbeddings | `from swarm_models.openai_embeddings import OpenAIEmbeddings` |\n| OpenAITTS | `from swarm_models.openai_tts import OpenAITTS` |\n| GooglePalm | `from swarm_models.palm import GooglePalm as Palm` |\n| Anthropic | `from swarm_models.popular_llms import Anthropic as Anthropic` |\n| AzureOpenAI | `from swarm_models.popular_llms import AzureOpenAILLM as AzureOpenAI` |\n| Cohere | `from swarm_models.popular_llms import CohereChat as Cohere` |\n| OctoAIChat | `from swarm_models.popular_llms import OctoAIChat` |\n| OpenAIChat | `from swarm_models.popular_llms import OpenAIChatLLM as OpenAIChat` |\n| OpenAILLM | `from swarm_models.popular_llms import OpenAILLM as OpenAI` |\n| Replicate | `from swarm_models.popular_llms import ReplicateChat as Replicate` |\n| QwenVLMultiModal | `from swarm_models.qwen import QwenVLMultiModal` |\n| FireWorksAI | `from swarm_models.popular_llms import FireWorksAI` |\n| Vilt | `from swarm_models.vilt import Vilt` |\n| TogetherLLM | `from swarm_models.together_llm import TogetherLLM` |\n| LiteLLM | `from swarm_models.lite_llm_model import LiteLLM` |\n| OpenAIFunctionCaller | `from swarm_models.openai_function_caller import OpenAIFunctionCaller` |\n| OllamaModel | `from swarm_models.ollama_model import OllamaModel` |\n| GroundedSAMTwo | `from swarm_models.sam_two import GroundedSAMTwo` |\n\n\n---\n\n## **Support & Contributions**\n\n- **Documentation**: Comprehensive guides, API references, and best practices are available in our official [Documentation](https://docs.swarms.world).\n- **GitHub**: Explore the code, report issues, and contribute to the project via our [GitHub repository](https://github.com/The-Swarm-Corporation/swarm-models).\n\n---\n\n## **License**\n\nSwarm Models is released under the [MIT License](https://github.com/The-Swarm-Corporation/swarm-models/LICENSE).\n\n---\n\n\n# Todo\n\n- [ ] Add cohere models command r\n- [ ] Add gemini and google ai studio\n- [ ] Integrate ollama extensively\n",
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