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To use Llama 2 with LlamaIndex, you have several options depending on your specific requirements and setup. Here's a summary of how you can integrate Llama 2:
- Using Ollama (for local deployment):
If you have Ollama () installed and running, you can use Llama 2 locally with LlamaIndex like this:
from llama_index.llms.ollama import Ollama
from llama_index.core import Settings
Settings.llm = Ollama(model="llama2", request_timeout=60.0)
()
- Using LlamaCPP:
You can use the llama-cpp-python () library to run Llama 2 locally. This method requires a decent GPU, ideally an A100 with at least 40GB of memory. Here's a basic setup:
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import messages_to_prompt, completion_to_prompt
# Initialize the LlamaCPP model (you'll need to specify the model path)
llm = LlamaCPP(model_path="path/to/your/llama2/model")
()
- Using Llama API:
If you prefer a hosted solution, you can use the Llama API (), which provides a hosted API for Llama 2 with function calling support:
from llama_index.llms.llama_api import LlamaAPI
api_key = "LL-your-key"
llm = LlamaAPI(api_key=api_key)
()
Remember to install the necessary dependencies for your chosen method. For example, for LlamaCPP:
