Tutoriales

TL;DR:Local LLM models run on your hardware without API calls. Popular options include Llama 3.1 (8B-405B), Mistral 7B, Phi-3, and […]

There is no “LM Studio Chicago” server, region, or edition. LM Studio is a desktop app for Windows, macOS and

There is no dedicated Ollama integration. You connect OpenClaw to Ollama through Ollama’s OpenAI-compatible API at http://localhost:11434/v1, treating it as

TL;DR:Safetensors is a file format for storing machine learning model weights that prevents arbitrary code execution vulnerabilities present in pickle-based

“vLLM omni” almost always refers to running omni-modal models (text + vision + audio + video) on the vLLM inference

Claude Code does not natively support Ollama — it expects the Anthropic API. To use local models, you run a

TL;DR:LM Studio is a free desktop application for running large language models locally on your computer without coding or command-line workDownload models directly from Hugging Face through the built-in browser, supports GGUF format with automatic quantization selectionIncludes chat UI, local API server (OpenAI-compatible), and automatic hardware acceleration (CUDA, Metal, CPU)Requires sufficient VRAM/RAM based on model size—typically 8GB minimum for 7B models, 24GB+ for 13B modelsLM Studio is a desktop application that lets you download, run, and interact with large language models on your own computer.

TL;DRgpt-oss:20b runs in ~16GB memory (fits most gaming GPUs), gpt-oss:120b needs ~70GB (single 80GB GPU or split across consumer cards)Install with ollama pull gpt-oss:20b or ollama pull gpt-oss:120b, then run with ollama run gpt-oss:20bBoth variants use MXFP4 quantisation at 4.25 bits per parameter and support 128K context windowsReleased by OpenAI as open-weight models in partnership with Ollama, comparable to Llama 3.1 and Qwen 2.5 in quality OpenAI released gpt-oss as open-weight models in August 2025, distributed exclusively through Ollama.

TL;DR:There is no Gemma 4 release as of August 2026.

A Hugging Face dataset is a structured data collection hosted on the Hugging Face Hub, searchable at huggingface.co/datasets — over 300,000 public datasets as of 2026. Load any dataset in one line: from datasets import load_dataset; ds = load_dataset(‘stanfordnlp/imdb’)Each dataset ships with typed splits (train/validation/test), a features schema (text, image, audio, labels), and optional streaming for terabyte-scale files. Push your own data with ds.push_to_hub(‘your-username/your-dataset’) after running huggingface-cli login. A Hugging Face dataset is a versioned, structured data collection stored on the Hugging Face Hub and consumed through the datasets Python library.

DeepSite is a free Hugging Face Space athuggingface.co/spaces/enricoros/deepsite that turns a text prompt into a complete, self-contained HTML web app. No installation, no account required to try — open the URL, describe your app, click Generate, get a live preview and a downloadable .html file. The output is a single file with inline CSS and JavaScript; host it on Hugging Face Spaces, GitHub Pages, Netlify, or any static server. Duplicate the Space and add your own Hugging Face API token to avoid queue times and pick your preferred model. Hugging Face DeepSite is an open-source, browser-based web app generator.

Run ollama ps while a model is loaded — the PROCESSOR column tells you whether Ollama is using GPU or CPU.The most common fix on NVIDIA is installing or updating the host driver so nvidia-smi sees the card, then restarting the Ollama service. If the model is larger than your VRAM, Ollama offloads layers to CPU — use the VRAM calculator to check whether your model fits before downloading it. AMD, Docker, and WSL2 each require platform-specific steps covered below.