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from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent import MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
# Initialize the Llama model
llama_model = Llama("Arcee-Spark-GGUF/Arcee-Spark-Q4_K_M.gguf", n_batch=1024, n_threads=10, n_gpu_layers=33, n_ctx=2048, verbose=False)
# Create the provider
provider = LlamaCppPythonProvider(llama_model)
# Create the agent
agent = LlamaCppAgent(
provider,
system_prompt="You are a helpful assistant.",
predefined_messages_formatter_type=MessagesFormatterType.CHATML,
debug_output=True
)
# Set provider settings
settings = provider.get_provider_default_settings()
settings.max_tokens = 2000
settings.stream = True
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent import MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
# Initialize the Llama model
llama_model = Llama("Arcee-Spark-GGUF/Arcee-Spark-Q4_K_M.gguf", n_batch=1024, n_threads=10, n_gpu_layers=33, n_ctx=2048, verbose=False)
# Create the provider
provider = LlamaCppPythonProvider(llama_model)
# Create the agent
agent = LlamaCppAgent(
provider,
system_prompt="You are a helpful assistant.",
predefined_messages_formatter_type=MessagesFormatterType.CHATML,
debug_output=True
)
# Set provider settings
settings = provider.get_provider_default_settings()
settings.max_tokens = 2000
settings.stream = True
def send_to_llm(provider, msg_list):
try:
provider.apply_settings(settings)
# Call get_chat_response without the settings parameter
response = agent.get_chat_response(msg_list, llm_sampling_settings=settings)
return response.content, None # We don't have usage info in this case
except Exception as e:
print(f"Error in send_to_llm: {str(e)}")
return f"Error: {str(e)}", None
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