<?xml version="1.0" encoding="UTF-8"?>
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    <title>llama</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/754/feed"/>
    <updated>2026-06-14T21:16:00+00:00</updated>
    <id>https://links.biapy.com/guest/tags/754/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12896</id>
            <title type="text"><![CDATA[LLMKube]]></title>
            <link rel="alternate" href="https://llmkube.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12896"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Kubernetes for Local LLMs.

A Kubernetes operator for self-hosted LLM inference. vLLM, llama.cpp, TGI, NVIDIA, Apple Silicon.

- [LLMKube @ GitHub](https://github.com/defilantech/llmkube).

Related contents:

- [Run LLMs on Kubernetes with LLMKube @ That DevOps Guy&amp;#039;s YouTube](https://www.youtube.com/watch?v=xdMtc8jm88Q).]]>
            </summary>
            <updated>2026-06-02T06:48:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12336</id>
            <title type="text"><![CDATA[llmfit]]></title>
            <link rel="alternate" href="https://github.com/AlexsJones/llmfit" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12336"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Hundreds of models &amp;amp; providers. One command to find what runs on your hardware.

A terminal tool that right-sizes LLM models to your system&amp;#039;s RAM, CPU, and GPU. Detects your hardware, scores each model across quality, speed, fit, and context dimensions, and tells you which ones will actually run well on your machine.

Ships with an interactive TUI (default) and a classic CLI mode. Supports multi-GPU setups, MoE architectures, dynamic quantization selection, speed estimation, and local runtime providers (Ollama, llama.cpp, MLX, Docker Model Runner, LM Studio).

Related contents:

- [\#130 - News avril 2026, Cursor et Copilot dans la tourmente, Axios compromis et Arrow JS @ Double Slash :fr:](https://double-slash.dev/podcasts/news-avril26/).
- [llmfit - L&amp;#039;outil qui sait quel LLM votre PC peut encaisser @ Korben :fr:](https://korben.info/llmfit-trouver-llm-compatible-hardware.html).
- [LLMFit : Quel LLM faire tourner sur votre ordinateur ? @ Geeek :fr:](https://www.geeek.org/llmfit-llm-local/).]]>
            </summary>
            <updated>2026-04-30T11:15:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10124</id>
            <title type="text"><![CDATA[Paddler]]></title>
            <link rel="alternate" href="https://paddler.intentee.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10124"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-source LLMOps platform for hosting and scaling AI in your own infrastructure 🏓🦙 
 Paddler is an open-source LLMOps platform that lets teams run inference and deploy LLMs on their own infrastructure. 

- [Paddler @ GitHub](https://github.com/intentee/paddler).]]>
            </summary>
            <updated>2025-09-11T10:33:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/673</id>
            <title type="text"><![CDATA[LLM]]></title>
            <link rel="alternate" href="https://llm.datasette.io/en/stable/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/673"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A CLI utility and Python library for interacting with Large Language Models.

A CLI tool and Python library for interacting with OpenAI, Anthropic’s Claude, Google’s Gemini, Meta’s Llama and dozens of other Large Language Models, both via remote APIs and with models that can be installed and run on your own machine.

- [LLM @ GitHub](https://github.com/simonw/llm/).

Related contents:

- [Large Language Models can run tools in your terminal with LLM 0.26 @ Simon Willison’s Weblog](https://simonwillison.net/2025/May/27/llm-tools/).
- [Using AI Without Leaving the Terminal: A Guide to llm @ Ashwin&amp;#039;s Blog](https://kashw1n.com/blog/llm-cli/).
- [Evaluating LLMs for my personal use case @ Graham King](https://darkcoding.net/software/personal-ai-evals-aug-2025/).]]>
            </summary>
            <updated>2025-09-04T16:51:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2511</id>
            <title type="text"><![CDATA[🌟 Awesome LLM Apps]]></title>
            <link rel="alternate" href="https://github.com/Shubhamsaboo/awesome-llm-apps" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2511"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models. 

A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and even open-source models like LLaMA that you can run locally on your computer.]]>
            </summary>
            <updated>2025-08-28T22:54:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4389</id>
            <title type="text"><![CDATA[llamafile]]></title>
            <link rel="alternate" href="https://mozilla-ai.github.io/llamafile/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4389"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Distribute and run LLMs with a single file.

Our goal is to make open LLMs much more accessible to both developers and end users. We&amp;#039;re doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a &amp;quot;llamafile&amp;quot;) that runs locally on most computers, with no installation.

- [llamafile @ GitHub](https://github.com/mozilla-ai/llamafile).

Related contents:

- [LLaMA Now Goes Faster on CPUs @ justine&amp;#039;s web page](https://justine.lol/matmul/).
- [Justine Tunney booste encore une fois les performances de llama.cpp @ Korben :fr:](https://korben.info/justine-tunney-booste-performances-llama-cpp-nouveaux-kernels-algebre-lineaire.html).
- [Llamafile - Exécutez des modèles de langage en un seul fichier ! @ Korben :fr:](https://korben.info/llamafile-executez-modeles-langage-fichier.html).]]>
            </summary>
            <updated>2026-03-23T16:22:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4496</id>
            <title type="text"><![CDATA[Unsloth AI]]></title>
            <link rel="alternate" href="https://unsloth.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4496"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Finetune AI &amp;amp; LLMs faster.
 Web UI for training and running open models like Gemma 4, Qwen3.6, DeepSeek, gpt-oss locally. 

Unslow AI training &amp;amp; finetuning Get 30x faster with unsloth.  5X faster 60% less memory QLoRA finetuning. Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory!

- [Unsloth @ GitHub](https://github.com/unslothai/unsloth).

Related contents:

- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).
- [7 Lessons from building a small-scale AI application @ Richard Li](https://www.thelis.org/blog/lessons-from-ai).]]>
            </summary>
            <updated>2026-04-30T09:05:43+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4497</id>
            <title type="text"><![CDATA[Ollama]]></title>
            <link rel="alternate" href="https://ollama.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4497"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Get up and running with large language models, locally.

Run Llama 2, Code Llama, Mistral, Gemma, and other models. Customize and create your own.

- [Ollama @ GitHub](https://github.com/ollama/ollama).

Related contents:

- [Local RAG with Ollama, Mistral, and Turso @ Turso&amp;#039;s blog](https://turso.tech/blog/local-rag-with-ollama-and-turso-sqlite).
- [S4E10 - Quel destin pour l’Apple Vision Pro ? @ Underscore_&amp;#039;s Acast :fr:](https://shows.acast.com/micode-underscore/episodes/s4e10-quel-destin-pour-lapple-vision-pro).
- [Ollama Course – Build AI Apps Locally @ freeCodeCamp.org&amp;#039;s YouTube](https://www.youtube.com/watch?v=GWB9ApTPTv4).
- [Detecting Exposed LLM Servers: A Shodan Case Study on Ollama @ Cisco Blogs](https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama).
- [Ollama - 14 000 serveurs IA laissés en libre-service sur Internet @ Korben :fr:](https://korben.info/ollama-serveurs-vulnerabilites-secrete.html).
- [Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/brent-roose-est-linvite-du-n147-de?open=false#%C2%A7faire-tourner-un-llm-localement-sur-votre-ordinateur).
- [The Ultimate Beginner&amp;#039;s Guide to Self-Hosting Your Own AI @ Arsturn](https://www.arsturn.com/blog/the-ultimate-beginners-guide-to-self-hosting-your-own-ai).
- [How to Run and Customize LLMs Locally with Ollama @ freeCodeCamp](https://www.freecodecamp.org/news/run-and-customize-llms-locally-with-ollama/).
- [LLMs on Kubernetes Part 1: Understanding the threat model @ CNCF](https://www.cncf.io/blog/2026/03/30/llms-on-kubernetes-part-1-understanding-the-threat-model/).
- [Faire tourner un modèle IA chez soi avec Ollama @ DomoPi :fr:](https://domopi.eu/faire-tourner-un-modele-ia-chez-soi-avec-ollama/).
- [Using AI for Terraform: running locally with Langflow, OpenSearch, &amp;amp; Ollama @ Rosemary Wang&amp;#039;s dev.to](https://dev.to/joatmon08/using-ai-for-terraform-running-a-locally-with-langflow-opensearch-ollama-5co6).]]>
            </summary>
            <updated>2026-04-08T05:59:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4934</id>
            <title type="text"><![CDATA[Text generation web UI]]></title>
            <link rel="alternate" href="https://github.com/oobabooga/text-generation-webui" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4934"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Gradio web UI for Large Language Models. Supports transformers, GPTQ, llama.cpp (GGUF), Llama models.]]>
            </summary>
            <updated>2025-08-29T05:39:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5155</id>
            <title type="text"><![CDATA[Ollama]]></title>
            <link rel="alternate" href="https://ollama.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5155"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Get up and running with large language models, locally.
Run Llama 2 and other models on macOS. Customize and create your own.

- [Ollama @ GitHub](https://github.com/jmorganca/ollama).
- [6 outils de FOU pour les DEVS 🤯 @ YoanDev&amp;#039;s YouTube](https://www.youtube.com/watch?v=x0niOhjzkxw).]]>
            </summary>
            <updated>2025-08-29T06:16:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5423</id>
            <title type="text"><![CDATA[RedPajama-Data]]></title>
            <link rel="alternate" href="https://github.com/togethercomputer/RedPajama-Data" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5423"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The RedPajama-Data repository contains code for preparing large datasets for training large language models.
RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset.]]>
            </summary>
            <updated>2025-08-29T07:00:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5452</id>
            <title type="text"><![CDATA[gpt4all]]></title>
            <link rel="alternate" href="https://github.com/nomic-ai/gpt4all" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5452"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.

Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa]]>
            </summary>
            <updated>2025-08-29T07:05:55+00:00</updated>
        </entry>
    </feed>
