documentation

DOCS/agents/README.md

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+# Swarms Documentation
+====================
+
+## Language Models
+---------------
+
+Language models are the driving force of our agents. They are responsible for generating text based on a given prompt. We currently support two types of language models: Anthropic and HuggingFace.
+
+### Anthropic
+
+The `Anthropic` class is a wrapper for the Anthropic large language models.
+
+#### Initialization
+
+```
+Anthropic(model="claude-2", max_tokens_to_sample=256, temperature=None, top_k=None, top_p=None, streaming=False, default_request_timeout=None)
+```
+
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+
+##### Parameters
+
+-   `model` (str, optional): The name of the model to use. Default is "claude-2".
+-   `max_tokens_to_sample` (int, optional): The maximum number of tokens to sample. Default is 256.
+-   `temperature` (float, optional): The temperature to use for the generation. Higher values result in more random outputs.
+-   `top_k` (int, optional): The number of top tokens to consider for the generation.
+-   `top_p` (float, optional): The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling.
+-   `streaming` (bool, optional): Whether to use streaming mode. Default is False.
+-   `default_request_timeout` (int, optional): The default request timeout in seconds. Default is 600.
+
+##### Example
+
+```
+anthropic = Anthropic(model="claude-2", max_tokens_to_sample=100, temperature=0.8)
+```
+
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+
+#### Generation
+
+```
+anthropic.generate(prompt, stop=None)
+```
+
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+
+##### Parameters
+
+-   `prompt` (str): The prompt to use for the generation.
+-   `stop` (list, optional): A list of stop sequences. The generation will stop if one of these sequences is encountered.
+
+##### Returns
+
+-   `str`: The generated text.
+
+##### Example
+
+```
+prompt = "Once upon a time"
+stop = ["The end"]
+print(anthropic.generate(prompt, stop))
+```
+
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+
+### HuggingFaceLLM
+
+The `HuggingFaceLLM` class is a wrapper for the HuggingFace language models.
+
+#### Initialization
+
+```
+HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None)
+```
+
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+
+##### Parameters
+
+-   `model_id` (str): The ID of the model to use.
+-   `device` (str, optional): The device to use for the generation. Default is "cuda" if available, otherwise "cpu".
+-   `max_length` (int, optional): The maximum length of the generated text. Default is 20.
+-   `quantize` (bool, optional): Whether to quantize the model. Default is False.
+-   `quantization_config` (dict, optional): The configuration for the quantization.
+
+##### Example
+
+```
+huggingface = HuggingFaceLLM(model_id="gpt2", device="cpu", max_length=50)
+```
+
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+
+#### Generation
+
+```
+huggingface.generate(prompt_text: str, max_length: int = None)
+```
+
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+
+##### Parameters
+
+-   `prompt_text` (str): The prompt to use for the generation.
+-   `max_length` (int, optional): The maximum length of the generated text. If not provided, the default value specified during initialization is used.
+
+##### Returns
+
+-   `str`: The generated text.
+
+##### Example
+
+```
+prompt = "Once upon a time"
+print(huggingface.generate(prompt))
+```

images/Swarms.md

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+# Agents
+* Agents are the fundamental building blocks of a swarm, they are indivi

swarms/utils/main.py

@@ -400,7 +400,7 @@ class FileHandler:
 
 #############===========================>
 
-from swarms.agents.prompts.prompts import DATAFRAME_PROMPT
+from swarms.agents.models.prompts.prompts import DATAFRAME_PROMPT
 
 import pandas as pd
 class CsvToDataframe(BaseHandler):