56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import argparse
|
|
|
|
from llama_cpp import Llama
|
|
|
|
from langchain.llms.base import LLM
|
|
from typing import Optional, List, Mapping, Any
|
|
|
|
|
|
class LlamaLLM(LLM):
|
|
model_path: str
|
|
llm: Llama
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
return "llama-cpp-python"
|
|
|
|
def __init__(self, model_path: str, **kwargs: Any):
|
|
model_path = model_path
|
|
llm = Llama(model_path=model_path)
|
|
super().__init__(model_path=model_path, llm=llm, **kwargs)
|
|
|
|
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
|
response = self.llm(prompt, stop=stop or [])
|
|
return response["choices"][0]["text"]
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
return {"model_path": self.model_path}
|
|
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-m", "--model", type=str, default="../models/7B/ggml-models.bin")
|
|
args = parser.parse_args()
|
|
|
|
# Load the model
|
|
llm = LlamaLLM(model_path=args.model)
|
|
|
|
# Basic Q&A
|
|
answer = llm(
|
|
"Question: What is the capital of France? Answer: ", stop=["Question:", "\n"]
|
|
)
|
|
print(f"Answer: {answer.strip()}")
|
|
|
|
# Using in a chain
|
|
from langchain.prompts import PromptTemplate
|
|
from langchain.chains import LLMChain
|
|
|
|
prompt = PromptTemplate(
|
|
input_variables=["product"],
|
|
template="\n\n### Instruction:\nWrite a good name for a company that makes {product}\n\n### Response:\n",
|
|
)
|
|
chain = LLMChain(llm=llm, prompt=prompt)
|
|
|
|
# Run the chain only specifying the input variable.
|
|
print(chain.run("colorful socks"))
|