chore: import upstream snapshot with attribution

examples/models/cerebras_example.py

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+"""
+Example of using Cerebras with browser-use.
+
+To use this example:
+1. Set your CEREBRAS_API_KEY environment variable
+2. Run this script
+
+Cerebras integration is working great for:
+- Direct text generation
+- Simple tasks without complex structured output
+- Fast inference for web automation
+
+Available Cerebras models (9 total):
+Small/Fast models (8B-32B):
+- cerebras_llama3_1_8b (8B parameters, fast)
+- cerebras_llama_4_scout_17b_16e_instruct (17B, instruction-tuned)
+- cerebras_llama_4_maverick_17b_128e_instruct (17B, extended context)
+- cerebras_qwen_3_32b (32B parameters)
+
+Large/Capable models (70B-480B):
+- cerebras_llama3_3_70b (70B parameters, latest version)
+- cerebras_gpt_oss_120b (120B parameters, OpenAI's model)
+- cerebras_qwen_3_235b_a22b_instruct_2507 (235B, instruction-tuned)
+- cerebras_qwen_3_235b_a22b_thinking_2507 (235B, complex reasoning)
+- cerebras_qwen_3_coder_480b (480B, code generation)
+
+Note: Cerebras has some limitations with complex structured output due to JSON schema compatibility.
+"""
+
+import asyncio
+import os
+
+from browser_use import Agent
+
+
+async def main():
+	# Set your API key (recommended to use environment variable)
+	api_key = os.getenv('CEREBRAS_API_KEY')
+	if not api_key:
+		raise ValueError('Please set CEREBRAS_API_KEY environment variable')
+
+	# Option 1: Use the pre-configured model instance (recommended)
+	from browser_use import llm
+
+	# Choose your model:
+	# Small/Fast models:
+	# model = llm.cerebras_llama3_1_8b      # 8B, fast
+	# model = llm.cerebras_llama_4_scout_17b_16e_instruct  # 17B, instruction-tuned
+	# model = llm.cerebras_llama_4_maverick_17b_128e_instruct  # 17B, extended context
+	# model = llm.cerebras_qwen_3_32b       # 32B
+
+	# Large/Capable models:
+	# model = llm.cerebras_llama3_3_70b     # 70B, latest
+	# model = llm.cerebras_gpt_oss_120b      # 120B, OpenAI's model
+	# model = llm.cerebras_qwen_3_235b_a22b_instruct_2507  # 235B, instruction-tuned
+	model = llm.cerebras_qwen_3_235b_a22b_thinking_2507  # 235B, complex reasoning
+	# model = llm.cerebras_qwen_3_coder_480b  # 480B, code generation
+
+	# Option 2: Create the model instance directly
+	# model = ChatCerebras(
+	#     model="qwen-3-coder-480b",  # or any other model ID
+	#     api_key=os.getenv("CEREBRAS_API_KEY"),
+	#     temperature=0.2,
+	#     max_tokens=4096,
+	# )
+
+	# Create and run the agent with a simple task
+	task = 'Explain the concept of quantum entanglement in simple terms.'
+	agent = Agent(task=task, llm=model)
+
+	print(f'Running task with Cerebras {model.name} (ID: {model.model}): {task}')
+	history = await agent.run(max_steps=3)
+	result = history.final_result()
+
+	print(f'Result: {result}')
+
+
+if __name__ == '__main__':
+	asyncio.run(main())