cerebas example md

cerebas_example.py

@@ -0,0 +1,13 @@
+from swarms.structs.agent import Agent
+
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    max_loops=4,
+    model_name="cerebras/llama3-70b-instruct",
+    dynamic_temperature_enabled=True,
+    interactive=False,
+    output_type="all",
+)
+
+agent.run("Conduct an analysis of the best real undervalued ETFs")

docs/mkdocs.yml

@@ -347,6 +347,7 @@ nav:
       - OpenAIChat: "swarms/models/openai.md"
       - OpenAIFunctionCaller: "swarms/models/openai_function_caller.md"
       - Groq: "swarms/models/groq.md"
+      - Cerebras: "swarms/models/cerebas_example.md"
     - MultiModal Models:
       - BaseMultiModalModel: "swarms/models/base_multimodal_model.md"
       - Multi Modal Models Available: "swarms/models/multimodal_models.md"

docs/swarms/models/cerebas_example.md

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+# Using Cerebras LLaMA with Swarms
+
+This guide demonstrates how to create and use an AI agent powered by the Cerebras LLaMA 3 70B model using the Swarms framework.
+
+## Prerequisites
+
+- Python 3.7+
+- Swarms library installed (`pip install swarms`)
+
+## Step-by-Step Guide
+
+### 1. Import Required Module
+
+```python
+from swarms.structs.agent import Agent
+```
+
+This imports the `Agent` class from Swarms, which is the core component for creating AI agents.
+
+### 2. Create an Agent Instance
+
+```python
+agent = Agent(
+    agent_name="Financial-Analysis-Agent",
+    agent_description="Personal finance advisor agent",
+    max_loops=4,
+    model_name="cerebras/llama3-70b-instruct",
+    dynamic_temperature_enabled=True,
+    interactive=False,
+    output_type="all",
+)
+```
+
+Let's break down each parameter:
+
+- `agent_name`: A descriptive name for your agent (here, "Financial-Analysis-Agent")
+
+- `agent_description`: A brief description of the agent's purpose
+
+- `max_loops`: Maximum number of interaction loops the agent can perform (set to 4)
+
+- `model_name`: Specifies the Cerebras LLaMA 3 70B model to use
+
+- `dynamic_temperature_enabled`: Enables dynamic adjustment of temperature for varied responses
+
+- `interactive`: When False, runs without requiring user interaction
+
+- `output_type`: Set to "all" to return complete response information
+
+### 3. Run the Agent
+
+```python
+agent.run("Conduct an analysis of the best real undervalued ETFs")
+```
+
+This command:
+
+1. Activates the agent
+
+2. Processes the given prompt about ETF analysis
+
+3. Returns the analysis based on the model's knowledge
+
+## Notes
+
+- The Cerebras LLaMA 3 70B model is a powerful language model suitable for complex analysis tasks
+
+- The agent can be customized further with additional parameters
+
+- The `max_loops=4` setting prevents infinite loops while allowing sufficient processing depth
+
+- Setting `interactive=False` makes the agent run autonomously without user intervention
+
+## Example Output
+
+The agent will provide a detailed analysis of undervalued ETFs, including:
+
+- Market analysis
+
+- Performance metrics
+
+- Risk assessment
+
+- Investment recommendations
+
+Note: Actual output will vary based on current market conditions and the model's training data.