From db2e72b788db358f23ed6a1ebcf1c7fd9ae9d345 Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Sat, 5 Aug 2023 18:40:46 -0400
Subject: [PATCH] base language model imports

---
 DOCS/agents/MODELS.md | 186 ++++++++++++++++++++++++++++++++++++++++++
 swarms/__init__.py    |   5 ++
 2 files changed, 191 insertions(+)
 create mode 100644 DOCS/agents/MODELS.md

diff --git a/DOCS/agents/MODELS.md b/DOCS/agents/MODELS.md
new file mode 100644
index 00000000..5573de8b
--- /dev/null
+++ b/DOCS/agents/MODELS.md
@@ -0,0 +1,186 @@
+# Models 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)
+
+```
+
+
+
+##### 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)
+
+```
+
+
+
+#### Generation
+
+```
+
+anthropic.generate(prompt, stop=None)
+
+```
+
+
+
+##### 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))
+
+```
+
+
+
+### 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)
+
+```
+
+
+
+##### 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)
+
+```
+
+
+
+#### Generation
+
+```
+
+huggingface.generate(prompt_text: str, max_length: int = None)
+
+```
+
+
+
+##### 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))
+
+```
+
+
+### Full Examples
+
+```python
+# Import the necessary classes
+
+from swarms import Anthropic, HuggingFaceLLM
+
+# Create an instance of the Anthropic class
+
+anthropic = Anthropic(model="claude-2", max_tokens_to_sample=100, temperature=0.8)
+
+# Use the Anthropic instance to generate text
+
+prompt = "Once upon a time"
+
+stop = ["The end"]
+
+print("Anthropic output:")
+
+print(anthropic.generate(prompt, stop))
+
+# Create an instance of the HuggingFaceLLM class
+
+huggingface = HuggingFaceLLM(model_id="gpt2", device="cpu", max_length=50)
+
+# Use the HuggingFaceLLM instance to generate text
+
+prompt = "Once upon a time"
+
+print("\nHuggingFaceLLM output:")
+
+print(huggingface.generate(prompt))
+
+```
+
diff --git a/swarms/__init__.py b/swarms/__init__.py
index 6af7aba1..d6714a42 100644
--- a/swarms/__init__.py
+++ b/swarms/__init__.py
@@ -2,3 +2,8 @@ from swarms.swarms.swarms import HierarchicalSwarm, swarm
 from swarms.workers.worker_node import WorkerNodeInitializer, WorkerNode, worker_node
 from swarms.boss.boss_node import BossNodeInitializer, BossNode
 
+#models
+from swarms.agents.models.anthropic import Anthropic
+from swarms.agents.models.huggingface import HuggingFaceLLM
+from swarms.agents.models.palm import GooglePalm
+from swarms.agents.models.petals import Petals
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