[FEAT][DOCS]

docs/mkdocs.yml

@@ -124,6 +124,7 @@ nav:
     - Language:
       - BaseLLM: "swarms/models/base_llm.md"
       - Overview: "swarms/models/index.md"
+      - Llava3: "swarms/models/llama3.md"
       - HuggingFaceLLM: "swarms/models/huggingface.md"
       - Anthropic: "swarms/models/anthropic.md"
       - OpenAI: "swarms/models/openai.md"

docs/swarms/models/llama3.md

@@ -0,0 +1,96 @@
+## Llava3
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from swarms.models.base_llm import BaseLLM
+
+
+class Llama3(BaseLLM):
+    """
+    Llama3 class represents a Llama model for natural language generation.
+
+        Args:
+            model_id (str): The ID of the Llama model to use.
+            system_prompt (str): The system prompt to use for generating responses.
+            temperature (float): The temperature value for controlling the randomness of the generated responses.
+            top_p (float): The top-p value for controlling the diversity of the generated responses.
+            max_tokens (int): The maximum number of tokens to generate in the response.
+            **kwargs: Additional keyword arguments.
+
+        Attributes:
+            model_id (str): The ID of the Llama model being used.
+            system_prompt (str): The system prompt for generating responses.
+            temperature (float): The temperature value for generating responses.
+            top_p (float): The top-p value for generating responses.
+            max_tokens (int): The maximum number of tokens to generate in the response.
+            tokenizer (AutoTokenizer): The tokenizer for the Llama model.
+            model (AutoModelForCausalLM): The Llama model for generating responses.
+
+        Methods:
+            run(task, *args, **kwargs): Generates a response for the given task.
+
+    """
+
+    def __init__(
+        self,
+        model_id="meta-llama/Meta-Llama-3-8B-Instruct",
+        system_prompt: str = None,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_tokens: int = 4000,
+        **kwargs,
+    ):
+        self.model_id = model_id
+        self.system_prompt = system_prompt
+        self.temperature = temperature
+        self.top_p = top_p
+        self.max_tokens = max_tokens
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+        )
+
+    def run(self, task: str, *args, **kwargs):
+        """
+        Generates a response for the given task.
+
+        Args:
+            task (str): The user's task or input.
+
+        Returns:
+            str: The generated response.
+
+        """
+        messages = [
+            {"role": "system", "content": self.system_prompt},
+            {"role": "user", "content": task},
+        ]
+
+        input_ids = self.tokenizer.apply_chat_template(
+            messages, add_generation_prompt=True, return_tensors="pt"
+        ).to(self.model.device)
+
+        terminators = [
+            self.tokenizer.eos_token_id,
+            self.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+        ]
+
+        outputs = self.model.generate(
+            input_ids,
+            max_new_tokens=self.max_tokens,
+            eos_token_id=terminators,
+            do_sample=True,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            *args,
+            **kwargs,
+        )
+        response = outputs[0][input_ids.shape[-1] :]
+        return self.tokenizer.decode(
+            response, skip_special_tokens=True
+        )
+```

docs/swarms/structs/agent_rearrange.md

@@ -114,9 +114,8 @@ rearrange(agents, flow, task)
 Here's an example of how to use the `AgentRearrange` class and the `rearrange` function:
 
 ```python
-from swarms import Agent
+from swarms import Agent, AgentRearrange, rearrange
 from typing import List
-from agent_rearrange import AgentRearrange, rearrange
 
 # Initialize the director agent
 director = Agent(