onprem 0.0.4

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

url = 'https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/resolve/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin'

llm = LLM(model_name=os.path.basename(url))
llm.download_model(url, ssl_verify=True ) # set to False if corporate firewall gives you problems

There is already a file Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin in /home/amaiya/onprem_data.
 Do you want to still download it? (Y/n) Y
[██████████████████████████████████████████████████]

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)

llama.cpp: loading model from /home/amaiya/onprem_data/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32000
llama_model_load_internal: n_ctx      = 2048
llama_model_load_internal: n_embd     = 4096
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 32
llama_model_load_internal: n_layer    = 32
llama_model_load_internal: n_rot      = 128
llama_model_load_internal: ftype      = 2 (mostly Q4_0)
llama_model_load_internal: n_ff       = 11008
llama_model_load_internal: model size = 7B
llama_model_load_internal: ggml ctx size =    0.08 MB
llama_model_load_internal: mem required  = 5407.72 MB (+ 1026.00 MB per state)
llama_new_context_with_model: kv self size  = 1024.00 MB


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')

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 prompt method

Loading new documents: 100%|██████████████████████| 2/2 [00:00<00:00, 11.58it/s]

Step 3: Answer Questions About the Documents

question = """Please answer the following question in a single sentence using only the provided context: 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)

 K-Train is an automation tool that augments and complements human engineers during machine learning workow.

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:
tifying examples that the model is getting the most wrong, and Explainable AI methods to
understand why mistakes were made.
3) Model-Application.
Both the model and the potentially complex set of steps re-
quired to preprocess raw data into the format expected by the model must be easily saved,
transferred to, and executed on new data in a production environment.
ktrain is a Python library for machine learning with the goal of presenting a simple,

4.> ./sample_data/ktrain_paper.pdf:
this may involve language-specific preprocessing (e.g., tokenization). In the case of images,
this may involve auto-normalizing pixel values in a way that a chosen model expects. In
the case of graphs, this may involve compiling attributes of nodes and links in the network
(Data61, 2018). All preprocessing methods in ktrain return a Preprocessor instance
that encapsulates all the preprocessing steps for a particular task, which can be employed

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 CUDABLAS 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.