The ModelParallelizer allows you to run multiple models concurrently, comparing their outputs. This feature enables you to easily compare the performance and results of different models, helping you make informed decisions about which model to use for your specific task.
Plug-and-Play Integration: The structure provides a seamless integration with various models, including OpenAIChat, Anthropic, Mixtral, and Gemini. You can easily plug in any of these models and start using them without the need for extensive modifications or setup.
```python
import os
from dotenv import load_dotenv
from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
- Modularity and Flexibility: BlocksList allows users to create custom swarms by adding or removing different classes or functions as blocks. This means users can easily tailor the functionality of their swarm to suit their specific needs.
- Ease of Management: With methods to add, remove, update, and retrieve blocks, BlocksList provides a straightforward way to manage the components of a swarm. This makes it easier to maintain and update the swarm over time.
- Enhanced Searchability: BlocksList offers methods to get blocks by various attributes such as name, type, ID, and parent-related properties. This makes it easier for users to find and work with specific blocks in a large and complex swarm.
```python
import os
from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer
from pydantic import BaseModel
from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
# Load the environment variables
load_dotenv()
# Get the environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")
# Tool Agent
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "[More agents is all you need](https://arxiv.org/pdf/2402.05120.pdf)
@ -1249,6 +1105,9 @@ print(output)
```
## `HierarhicalSwarm`
Coming soon...
---
@ -1265,6 +1124,7 @@ The swarms package has been meticlously crafted for extreme use-ability and unde
├── agents
├── artifacts
├── memory
├── schemas
├── models
├── prompts
├── structs
@ -1313,13 +1173,12 @@ Accelerate Bugs, Features, and Demos to implement by supporting us here:
## Docker Instructions
- [Learn More Here About Deployments In Docker]()
- [Learn More Here About Deployments In Docker](https://swarms.apac.ai/en/latest/docker_setup/)
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Kye Gomez
8 months ago