onprem 0.0.7

A tool for running on-premises large language models on non-public data

OnPrem

A tool for running large language models on-premises using non-public data

OnPrem is a simple Python package that makes it easier to run large language models (LLMs) on non-public or sensitive data and on machines with no internet connectivity (e.g., behind corporate firewalls). Inspired by the privateGPT GitHub repo and Simon Willison’s LLM command-line utility, OnPrem is designed to help integrate local LLMs into practical applications.

Install

Once installing PyTorch, you can install OnPrem with:

pip install onprem

For GPU support, see additional instructions below.

How to use

Setup

import os.path
from onprem import LLM

llm = LLM()

Send Prompts to the LLM to Solve Problems

This is an example of few-shot prompting, where we provide an example of what we want the LLM to do.

prompt = """Extract the names of people in the supplied sentences. Here is an example:
Sentence: James Gandolfini and Paul Newman were great actors.
People:
James Gandolfini, Paul Newman
Sentence:
I like Cillian Murphy's acting. Florence Pugh is great, too.
People:"""

saved_output = llm.prompt(prompt)

Cillian Murphy, Florence Pugh

Talk to Your Documents

Answers are generated from the content of your documents.

Step 1: Ingest the Documents into a Vector Database

llm.ingest('./sample_data')

2023-09-03 16:30:54.459509: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Loading new documents: 100%|██████████████████████| 2/2 [00:00<00:00, 17.16it/s]

Creating new vectorstore
Loading documents from ./sample_data
Loaded 11 new documents from ./sample_data
Split into 62 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Ingestion complete! You can now query your documents using the LLM.ask method

Step 2: Answer Questions About the Documents

question = """What is  ktrain?""" 
answer, docs = llm.ask(question)
print('\n\nReferences:\n\n')
for i, document in enumerate(docs):
    print(f"\n{i+1}.> " + document.metadata["source"] + ":")
    print(document.page_content)

 Ktrain is a low-code machine learning library designed to augment human
engineers in the machine learning workow by automating or semi-automating various
aspects of model training, tuning, and application. Through its use, domain experts can
leverage their expertise while still benefiting from the power of machine learning techniques.

References:



1.> ./sample_data/ktrain_paper.pdf:
lection (He et al., 2019). By contrast, ktrain places less emphasis on this aspect of au-
tomation and instead focuses on either partially or fully automating other aspects of the
machine learning (ML) workflow. For these reasons, ktrain is less of a traditional Au-
2

2.> ./sample_data/ktrain_paper.pdf:
possible, ktrain automates (either algorithmically or through setting well-performing de-
faults), but also allows users to make choices that best fit their unique application require-
ments. In this way, ktrain uses automation to augment and complement human engineers
rather than attempting to entirely replace them. In doing so, the strengths of both are
better exploited. Following inspiration from a blog post1 by Rachel Thomas of fast.ai

3.> ./sample_data/ktrain_paper.pdf:
with custom models and data formats, as well.
Inspired by other low-code (and no-
code) open-source ML libraries such as fastai (Howard and Gugger, 2020) and ludwig
(Molino et al., 2019), ktrain is intended to help further democratize machine learning by
enabling beginners and domain experts with minimal programming or data science experi-
4. http://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups
6

4.> ./sample_data/ktrain_paper.pdf:
ktrain: A Low-Code Library for Augmented Machine Learning
toML platform and more of what might be called a “low-code” ML platform. Through
automation or semi-automation, ktrain facilitates the full machine learning workflow from
curating and preprocessing inputs (i.e., ground-truth-labeled training data) to training,
tuning, troubleshooting, and applying models. In this way, ktrain is well-suited for domain
experts who may have less experience with machine learning and software coding. Where

Speeding Up Inference Using a GPU

The above example employed the use of a CPU.
If you have a GPU (even an older one with less VRAM), you can speed up responses.

Step 1: Install llama-cpp-python with CUBLAS support

CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python==0.1.69 --no-cache-dir

It is important to use the specific version shown above due to library incompatibilities.

Step 2: Use the n_gpu_layers argument with LLM

llm = LLM(model_name=os.path.basename(url), n_gpu_layers=128)

With the steps above, calls to methods like llm.prompt will offload computation to your GPU and speed up responses from the LLM.