[EXAMPLES]

examples/models/anthropic_example.py

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+import os
+
+from swarm_models.models import Anthropic
+
+model = Anthropic(anthropic_api_key=os.getenv("ANTHROPIC_API_KEY"))
+
+task = "What is quantum field theory? What are 3 books on the field?"
+
+print(model(task))

examples/models/azure_openai.py

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+from swarm_models.models import AzureOpenAI
+
+# Initialize Azure OpenAI
+model = AzureOpenAI()
+
+# Run the model
+model(
+    "Create a youtube script for a video on how to use the swarms"
+    " framework"
+)

examples/models/azure_openai_example.py

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+import os
+from dotenv import load_dotenv
+from swarm_models import AzureOpenAI
+
+# Load the environment variables
+load_dotenv()
+
+# Create an instance of the AzureOpenAI class
+model = AzureOpenAI(
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+    deployment_name=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
+    openai_api_version=os.getenv("OPENAI_API_VERSION"),
+    openai_api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+    azure_ad_token=os.getenv("AZURE_OPENAI_AD_TOKEN"),
+)
+
+# Define the prompt
+prompt = (
+    "Analyze this load document and assess it for any risks and"
+    " create a table in markdwon format."
+)
+
+# Generate a response
+response = model(prompt)
+print(response)

examples/models/cohere_example.py

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+from swarm_models.models import Cohere
+
+cohere = Cohere(model="command-light", cohere_api_key="")
+
+out = cohere("Hello, how are you?")

examples/models/dalle3_concurrent_example.py

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+"""
+
+User task ->> GPT4 for prompt enrichment ->> Dalle3V for image generation
+->> GPT4Vision for image captioning ->> Dalle3 better image
+
+"""
+
+import os
+
+from swarm_models.dalle3 import Dalle3
+
+api_key = os.environ["OPENAI_API_KEY"]
+
+dalle3 = Dalle3(openai_api_key=api_key, n=1)
+
+# task = "Swarm of robots working super industrial ambience concept art"
+
+# image_url = dalle3(task)
+
+tasks = ["A painting of a dog", "A painting of a cat"]
+results = dalle3.process_batch_concurrently(tasks)
+
+# print(results)

examples/models/dalle3_example.py

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+from swarm_models.dalle3 import Dalle3
+
+model = Dalle3()
+
+task = "A painting of a dog"
+img = model(task)

examples/models/example_gpt4vison.py

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+from swarm_models import GPT4VisionAPI
+
+# Initialize with default API key and custom max_tokens
+api = GPT4VisionAPI(max_tokens=1000)
+
+# Define the task and image URL
+task = "Describe the scene in the image."
+img = (
+    "/home/kye/.swarms/swarms/examples/Screenshot from 2024-02-20"
+    " 05-55-34.png"
+)
+
+# Run the GPT-4 Vision model
+response = api.run(task, img)
+
+# Print the model's response
+print(response)

examples/models/example_idefics.py

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+# Import the idefics model from the swarms.models module
+from swarm_models.models import Idefics
+
+# Create an instance of the idefics model
+model = Idefics()
+
+# Define user input with an image URL and chat with the model
+user_input = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
+response = model.chat(user_input)
+print(response)
+
+# Define another user input with an image URL and chat with the model
+user_input = (
+    "User: And who is that?"
+    " https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+)
+response = model.chat(user_input)
+print(response)
+
+# Set the checkpoint of the model to "new_checkpoint"
+model.set_checkpoint("new_checkpoint")
+
+# Set the device of the model to "cpu"
+model.set_device("cpu")
+
+# Set the maximum length of the chat to 200
+model.set_max_length(200)
+
+# Clear the chat history of the model
+model.clear_chat_history()

examples/models/example_kosmos.py

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+from swarm_models import Kosmos
+
+# Initialize the model
+model = Kosmos()
+
+# Generate
+out = model.run("Analyze the reciepts in this image", "docs.jpg")
+
+# Print the output
+print(out)

examples/models/example_qwenvlmultimodal.py

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+from swarm_models import QwenVLMultiModal
+
+# Instantiate the QwenVLMultiModal model
+model = QwenVLMultiModal(
+    model_name="Qwen/Qwen-VL-Chat",
+    device="cuda",
+    quantize=True,
+)
+
+# Run the model
+response = model(
+    "Hello, how are you?", "https://example.com/image.jpg"
+)
+
+# Print the response
+print(response)

examples/models/fire_works.py

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+from swarm_models.popular_llms import Fireworks
+import os
+
+# Initialize the model
+llm = Fireworks(
+    temperature=0.2,
+    max_tokens=3500,
+    openai_api_key=os.getenv("FIREWORKS_API_KEY"),
+)
+
+# Run the model
+response = llm("What is the meaning of life?")
+print(response)

examples/models/fuyu_example.py

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+from swarm_models.fuyu import Fuyu
+
+fuyu = Fuyu()
+
+# This is the default image, you can change it to any image you want
+out = fuyu("What is this image?", "images/swarms.jpeg")
+print(out)

examples/models/gemini_example.py

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+import os
+
+from dotenv import load_dotenv
+
+from swarm_models.gemini import Gemini
+
+load_dotenv()
+
+api_key = os.environ["GEMINI_API_KEY"]
+
+# Initialize the model
+model = Gemini(gemini_api_key=api_key)
+
+# Establish the prompt and image
+task = "What is your name"
+img = "images/github-banner-swarms.png"
+
+# Run the model
+out = model.run("What is your name?", img=img)
+print(out)

examples/models/gpt4_v_example.py

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+import os  # Import the os module for working with the operating system
+
+from dotenv import (
+    load_dotenv,  # Import the load_dotenv function from the dotenv module
+)
+
+from swarm_models import (
+    GPT4VisionAPI,  # Import the GPT4VisionAPI class from the swarms module
+)
+
+# Load the environment variables
+load_dotenv()
+
+# Get the API key from the environment variables
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Create an instance of the GPT4VisionAPI class with the API key and model name
+gpt4vision = GPT4VisionAPI(
+    openai_api_key=api_key,
+    model_name="gpt-4o",
+    max_tokens=1000,
+    openai_proxy="https://api.openai.com/v1/chat/completions",
+)
+
+# Define the URL of the image to analyze
+img = "ear.png"
+
+# Define the task to perform on the image
+task = "What is this image"
+
+# Run the GPT4VisionAPI on the image with the specified task
+answer = gpt4vision.run(task, img, return_json=True)
+
+# Print the answer
+print(answer)

examples/models/gpt_4o_mini.py

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+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(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)

examples/models/groq_model_exampole.py

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+import os
+from swarm_models import OpenAIChat
+
+
+# Example usage:
+api_key = os.getenv("GROQ_API_KEY")
+
+# Model
+model = OpenAIChat(
+    openai_api_base="https://api.groq.com/openai/v1",
+    openai_api_key=api_key,
+    model_name="llama-3.1-70b-versatile",
+    temperature=0.1,
+)
+
+
+out = model("What is the essence of quantum field theory?")
+print(out)

examples/models/hf/llama3.py

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+from swarm_models import Agent
+from swarm_models.prompts.finance_agent_sys_prompt import (
+    FINANCIAL_AGENT_SYS_PROMPT,
+)
+import torch
+from swarm_models import BaseLLM
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+
+class NvidiaLlama31B(BaseLLM):
+    # Load the tokenizer and model
+    def __init__(self, max_tokens: int = 2048):
+        self.max_tokens = max_tokens
+        model_path = "nvidia/Llama-3.1-Minitron-4B-Width-Base"
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+        device = "cuda"
+        dtype = torch.bfloat16
+        self.model = LlamaForCausalLM.from_pretrained(
+            model_path, torch_dtype=dtype, device_map=device
+        )
+
+    def run(self, task: str):
+        # Prepare the input text
+        inputs = self.tokenizer.encode(task, return_tensors="pt").to(
+            self.model.device
+        )
+
+        # Generate the output
+        outputs = self.model.generate(
+            inputs, max_length=self.max_tokens
+        )
+
+        # Decode and print the output
+        output_text = self.tokenizer.decode(outputs[0])
+        print(output_text)
+
+        return output_text
+
+
+# # Example usage:
+# model = NvidiaLlama31B()
+# out = model.run("What is the essence of quantum field theory?")
+# print(out)
+
+model = NvidiaLlama31B()
+
+# Initialize the agent
+agent = Agent(
+    agent_name="Financial-Analysis-Agent_sas_chicken_eej",
+    system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="finance_agent.json",
+    user_name="swarms_corp",
+    retry_attempts=1,
+    context_length=200000,
+    return_step_meta=True,
+    disable_print_every_step=True,
+    output_type="json",
+)
+
+
+out = agent.run(
+    "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+)
+print(out)

examples/models/huggingface_example.py

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+from swarm_models.models import HuggingfaceLLM
+
+model_id = "NousResearch/Yarn-Mistral-7b-128k"
+inference = HuggingfaceLLM(model_id=model_id)
+
+task = "Once upon a time"
+generated_text = inference(task)
+print(generated_text)

examples/models/idefics_example.py

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+# Import the idefics model from the swarms.models module
+from swarm_models.models import Idefics
+
+# Create an instance of the idefics model
+model = Idefics()
+
+# Define user input with an image URL and chat with the model
+user_input = (
+    "User: What is in this image?"
+    " https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"
+)
+response = model.chat(user_input)
+print(response)
+
+# Define another user input with an image URL and chat with the model
+user_input = (
+    "User: And who is that?"
+    " https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052"
+)
+response = model.chat(user_input)
+print(response)
+
+# Set the checkpoint of the model to "new_checkpoint"
+model.set_checkpoint("new_checkpoint")
+
+# Set the device of the model to "cpu"
+model.set_device("cpu")
+
+# Set the maximum length of the chat to 200
+model.set_max_length(200)
+
+# Clear the chat history of the model
+model.clear_chat_history()