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143 lines
4.7 KiB
Markdown
143 lines
4.7 KiB
Markdown
# Large language model
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_Language models_ are computational model that can predict sequences in natural language.<br/>
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Useful for speech recognition, machine translation, natural language generation, optical character recognition, route
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optimization, handwriting recognition, grammar induction, information retrieval, and other tasks.
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_Large_ language models are predominantly based on transformers trained on large datasets, frequently including texts
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scraped from the Internet.<br/>
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They have superseded recurrent neural network-based models.
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<!-- Remove this line to uncomment if used
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## Table of contents <!-- omit in toc -->
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1. [TL;DR](#tldr)
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1. [Reasoning](#reasoning)
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1. [Concerns](#concerns)
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1. [Run LLMs Locally](#run-llms-locally)
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1. [Further readings](#further-readings)
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1. [Sources](#sources)
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## TL;DR
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| FIXME | Creator |
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| --------- | ---------- |
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| [ChatGPT] | OpenAI |
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| [Claude] | Anthropic |
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| [Copilot] | Microsoft |
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| [Duck AI] | DuckDuckGo |
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| [Gemini] | Google |
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| [Grok] | X |
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| [Llama] | Meta |
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| [Mistral] | Mistral AI |
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<!-- Uncomment if used
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<details>
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<summary>Setup</summary>
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```sh
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```
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</details>
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-->
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<!-- Uncomment if used
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<details>
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<summary>Usage</summary>
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```sh
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```
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</details>
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-->
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<!-- Uncomment if used
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<details>
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<summary>Real world use cases</summary>
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```sh
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```
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</details>
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-->
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## Reasoning
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Standard is just autocompletion. Models just try to infer or recall what the most probable next word would be.
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Chain of Thought tells models to _show their work_. It _feels_ like the model is calculating or thinking.<br/>
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What it really does is just increasing the chances that the answer is correct by breaking the user's questions in
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smaller, more manageable steps, and solving on each of them before giving back the final answer.<br/>
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The result is more accurate, but it costs more tokens and requires a bigger context window.
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At some point we gave models the ability to execute commands. This way the model can use (or even create) them to get
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or check the answer, instead of just infer or recall it.
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The ReAct loop (reason+act) came next, where the model loops on the things above. Breaks the request in smaller steps,
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acts on them using functions if necessary, checks the results, updates the chain of thoughts, repeat until the request
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is satisfied.
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Next step is [agentic AI][agent].
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## Concerns
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- Lots of people currently thinks of LLMs as _real intelligence_, when it is not.
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- People currently gives too much credibility to LLM answers, and trust them more than they trust their teachers,
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accountants, lawyers or even doctors.
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- AI companies could bias their models to say specific things, subtly promote ideologies, influence elections, or even
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rewrite history in the mind of those who trust the LLMs.
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- Models can be vulnerable to specific attacks (e.g. prompt injection) that would change the LLM's behaviour, bias it,
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or hide malware in their tools.
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- People is using LLMs mindlessly too much, mostly due to the convenience they offer but also because they don't understand
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what those are or how they work. This is causing lack of critical thinking and overreliance.
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- Model training and execution requires resources that are normally not available to the common person. This encourages
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people to depend from, and hence give power to, AI companies.
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## Run LLMs Locally
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Refer:
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- [Local LLM Hosting: Complete 2026 Guide - Ollama, vLLM, LocalAI, Jan, LM Studio & More].
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- [Run LLMs Locally: 6 Simple Methods].
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[Ollama]| [Jan] |[LMStudio] | [Docker model runner] | [llama.cpp] | [vLLM] | [Llamafile]
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## Further readings
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### Sources
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- [Run LLMs Locally: 6 Simple Methods]
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- [OpenClaw: Who are you?]
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- [Local LLM Hosting: Complete 2026 Guide - Ollama, vLLM, LocalAI, Jan, LM Studio & More]
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<!--
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Reference
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═╬═Time══
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-->
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<!-- In-article sections -->
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<!-- Knowledge base -->
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[Agent]: agent.md
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[Claude]: claude/README.md
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[Docker model runner]: ../docker.md#running-llms-locally
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[Gemini]: gemini/README.md
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[llama.cpp]: llama.cpp.md
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[LMStudio]: lmstudio.md
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[Ollama]: ollama.md
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[vLLM]: vllm.md
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<!-- Files -->
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<!-- Upstream -->
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<!-- Others -->
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[ChatGPT]: https://chatgpt.com/
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[Copilot]: https://copilot.microsoft.com/
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[Duck AI]: https://duck.ai/
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[Grok]: https://grok.com/
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[Jan]: https://www.jan.ai/
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[Llama]: https://www.llama.com/
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[Llamafile]: https://github.com/mozilla-ai/llamafile
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[Local LLM Hosting: Complete 2026 Guide - Ollama, vLLM, LocalAI, Jan, LM Studio & More]: https://www.glukhov.org/post/2025/11/hosting-llms-ollama-localai-jan-lmstudio-vllm-comparison/
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[Mistral]: https://mistral.ai/
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[OpenClaw: Who are you?]: https://www.youtube.com/watch?v=hoeEclqW8Gs
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[Run LLMs Locally: 6 Simple Methods]: https://www.datacamp.com/tutorial/run-llms-locally-tutorial
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