CODEWITHSUNDEEP
pythonhuggingface-transformerslangchainlarge-language-model
ventisk1ze
ventisk1ze

Reputation: 91

What are my options for running LLMs locally from pretrained weights?

I have a cluster, that is not connected to the internet, although has a sort of weights repository available. I need to run LLM inference on it.

The only option that I found until now is using combination of transformers and langchain modules, but I don't want to tweak hyperparameters of models. I ran into ollama software, but I cannot install anything on cluster, except from python libs. So, naturally I wonder, what are my options for running LLM inference? And there is some more questions.

  1. Can I just install ollama-python package and not install their linux software? Or do I need both to run my inference?
  2. If I manage to install ollama on this cluster, how can I provide pretrained weights to the model? If it helps, they are stored in (sometime multiple) .bin files

Upvotes: 0

Views: 643

Answers (1)

j3ffyang
j3ffyang

Reputation: 2470

You don't really have to install ollama. Instead, you can directly run the LLM, for example, for mistral model locally

llm = GPT4All(
    model="/home/jeff/.cache/huggingface/hub/gpt4all/mistral-7b-openorca.Q4_0.gguf",
    device='gpu', n_threads=8,
    callbacks=callbacks, verbose=True)

Or for falcon

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

model_id = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipeline = pipeline(
    "text-generation",
    model=model_id,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    # trust_remote_code=True,
    device_map="auto",
    max_new_tokens=100,
    # max_length=200,
)


from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
llm = HuggingFacePipeline(pipeline=pipeline)

I have a 16G ram Nvidia 4090 installed on my laptop, which can support the above 2 models running locally.

Upvotes: 1

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