Should You Use Open Source Large Language Models?
Tags: llm, ai, machinelearning, opensource
Published: 2023-12-10 15:45:05

---
title: Should You Use Open Source Large Language Models?
published: true
tags: LLM, AI, ML, Opensource
canonical_url: https://firstfinger.in/open-source-large-language-models/
---

 ![Should You Use Open Source Large Language Models?](https://firstfinger.in/content/images/2023/12/Open-Source-Large-Language-Models.png)

Large language models (LLMs) powered by artificial intelligence are gaining immense popularity, with over 325,000 models available on Hugging Face. As more models emerge, a key question is whether to use proprietary or open-source LLMs.

## What are LLMs and How Do They Differ?

- [LLMs leverage deep learning and massive datasets to generate human-like text](https://firstfinger.in/large-language-models-llm-explained/)
- **Proprietary LLMs** are owned and controlled by a company
- **Open-source LLMs** are freely accessible for anyone to use and modify
- Proprietary models currently tend to be much larger in terms of parameters
- However, size isn't everything - smaller open-source models are rapidly catching up
- Community contributions empower the evolution of open-source LLMs 

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> **_🔖 Recommend:_** [_How Do (LLM) Large Language Models Work?_](https://firstfinger.in/large-language-models-llm-explained/)

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## Benefits of Open Source LLMs

- **Transparency** - Better visibility into [model architecture](https://firstfinger.in/monolithic-vs-microservices/), training data, output generation
- **Customization** through fine-tuning custom datasets for specific use cases
- **Community** contributions across diverse perspectives enable experimentation

### Use Cases

Open-source LLMs are being deployed across industries:

- Healthcare
  - Diagnostic assistance
  - Treatment optimization
- Finance
  - Applications like [FinGPT](https://github.com/AI4Finance-Foundation/FinGPT) for financial analysis
- Science
  - Models like NASA's trained on geospatial data

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## Leading Models on Hugging Face

The Hugging Face [model leaderboard's](https://huggingface.co/models) latest benchmarks.

![Should You Use Open Source Large Language Models?](https://firstfinger.in/content/images/2023/12/The-Hugging-Face-model-leaderboard.webp)
_Top LLMs on Hugging Face_

- Currently, variations on [Meta's **LLM2** lead - spanning with 7-70 billion parameters](https://ai.meta.com/llama/) and _commercially usable_
- Other top models include:
  - [**Mistral-7B**](https://huggingface.co/mistralai/Mistral-7B-v0.1) is a transformer model and beats Llama 2 13B in all the tests.
  - [**Deepseek LLM**](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) has been trained on a vast dataset of 2 trillion tokens in English and Chinese.

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> **_🔖 Recommend:_** [**What is Vector Database and How does it work?**](https://firstfinger.in/what-is-vector-database-and-how-does-it-work/)

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## Downside of Open-source LLMs

Despite advances, LLMs have concerning have 3 major limitations:

- **Inaccuracy** - Hallucinations from inaccurate/incomplete training data
- **Security** - Potential exposure of private data in outputs
- **Bias** - Embedding biases that skew outputs

**Mitigating these risks** in early-stage LLMs remains vital.


## The Bottom Line

Open-source big language models make AI more available to everyone. This widens who can use them. But risks are still there. Even so, putting information out in the open and letting users adjust models to their needs gives power to people across fields.