Metadata-Version: 2.1
Name: onprem
Version: 0.0.9
Summary: A tool for running on-premises large language models on non-public data
Home-page: https://github.com/amaiya/onprem
Author: Arun S. Maiya
Author-email: arun@maiya.net
License: Apache Software License 2.0
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# OnPrem.LLM

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

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

**OnPrem.LLM** 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](https://github.com/imartinez/privateGPT) GitHub repo and
Simon Willison’s [LLM](https://pypi.org/project/llm/) command-line
utility, **OnPrem.LLM** is designed to help integrate local LLMs into
practical applications.

## Install

Once [installing PyTorch](https://pytorch.org/get-started/locally/), you
can install **OnPrem.LLM** with:

``` sh
pip install onprem
```

For fast GPU-accelerated inference, see additional instructions below.

## How to use

### Setup

``` python
from onprem import LLM

llm = LLM()
```

By default, a 7B-parameter model is used. If `use_larger=True`, a
13B-parameter is used. You can also supply the URL to an LLM of your
choosing to [`LLM`](https://amaiya.github.io/onprem/core.html#llm) (see
code generation section below for an example). Currently, only models in
GGML format are supported. Future versions of **OnPrem.LLM** will
transition to the newer GGUF format.

### 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.

``` python
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

``` python
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

``` python
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

### Text to Code Generation

We’ll use the CodeUp LLM by supplying the URL and employ the particular
prompt format this model expects.

``` python
from onprem import LLM
url = 'https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/resolve/main/codeup-llama-2-13b-chat-hf.ggmlv3.q4_1.bin'
llm = LLM(url, n_gpu_layers=43) # see below for GPU information
```

Setup the prompt based on what [this model
expects](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML#prompt-template-alpaca)
(this is important):

``` python
template = """
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:"""
```

``` python
answer = llm.prompt('Write Python code to validate an email address.', prompt_template=template)
```


    Here is an example of Python code that can be used to validate an email address:
    ```
    import re

    def validate_email(email):
        # Use a regular expression to check if the email address is in the correct format
        pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        if re.match(pattern, email):
            return True
        else:
            return False

    # Test the validate_email function with different inputs
    print("Email address is valid:", validate_email("example@example.com"))  # Should print "True"
    print("Email address is invalid:", validate_email("example@"))  # Should print "False"
    print("Email address is invalid:", validate_email("example.com"))  # Should print "False"
    ```
    The code defines a function `validate_email` that takes an email address as input and uses a regular expression to check if the email address is in the correct format. The regular expression checks for an email address that consists of one or more letters, numbers, periods, hyphens, or underscores followed by the `@` symbol, followed by one or more letters, periods, hyphens, or underscores followed by a `.` and two to three letters.
    The function returns `True` if the email address is valid, and `False` otherwise. The code also includes some test examples to demonstrate how to use the function.

Let’s try out the code generated above.

``` python
import re
def validate_email(email):
    # Use a regular expression to check if the email address is in the correct format
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    if re.match(pattern, email):
        return True
    else:
        return False
print(validate_email('sam@@openai.com')) # bad email address
print(validate_email('sam@openai'))      # bad email address
print(validate_email('sam@openai.com'))  # good email address
```

    False
    False
    True

The generated code may sometimes need editing, but this one worked
out-of-the-box.

### 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

``` shell
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`](https://amaiya.github.io/onprem/core.html#llm)

``` python
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.
