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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("Qwen2-1.5B-Instruct-Abliterated-iMat-GGUF/Qwen2-1.5B-Instruct-Abliterated-Q8_0.gguf", n_batch=1024, n_threads=24, n_gpu_layers=33, n_ctx=4098, verbose=False)
# Create the provider
provider = LlamaCppPythonProvider(llama_model)
# Create the agent
agent = LlamaCppAgent(
provider,
system_prompt="You are a helpful assistant who's purpose is it to help users craft and edit datasets.",
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:
full_message = "\n".join([f"{msg['role']}: {msg['content']}" for msg in msg_list])
response = agent.get_chat_response(full_message, llm_sampling_settings=settings)
if isinstance(response, str):
return response, None
elif hasattr(response, 'content'):
return response.content, None
else:
return str(response), None
except Exception as e:
print(f"Error in send_to_llm: {str(e)}")
return f"Error: {str(e)}", None
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