#jupyter_notebook#chinese_llm#chinese_nlp#finetune#generative_ai#instruct_gpt#instruction_set#llama#llm#lora#open_models#open_source#open_source_models#qlora
AirLLM is a tool that lets you run very large AI models on computers with limited memory by using a smart layer-by-layer loading technique instead of traditional compression methods. You can run a 70-billion-parameter model on just 4GB of GPU memory, or even a 405-billion-parameter model on 8GB, without losing model quality. The benefit is that you can use powerful AI models on affordable hardware without expensive upgrades, and the tool also offers optional compression features that can speed up performance by up to 3 times while maintaining accuracy.
https://github.com/lyogavin/airllm
⚖El TS fija doctrina sobre la capacidad y competencia de los plenos municipales para pronunciarse sobre cuestiones políticas de carácter internacional
#local#jurisprudencia
https://laadministracionaldia.inap.es/noticia.asp?id=1228267
🚀Jan-v1: локальная 4B-модель для веба — опенсорсная альтернатива Perplexity Pro
📌Что умеет
- SimpleQA: 91% точности, чуть выше Perplexity Pro — и всё это полностью локально.
- Сценарии: быстрый веб-поиск и глубокое исследование (Deep Research).
Из чего сделана
- Базируется на Qwen3-4B-Thinking (контекст до 256k), дообучена в Jan на рассуждение и работу с инструментами.
Где запускать
- Jan, llama.cpp или vLLM.
Как включить поиск в Jan
- Settings → Experimental Features → On
- Settings → MCP Servers → включите поисковый MCP (например, Serper)
Модели
- Jan-v1-4B: https://huggingface.co/janhq/Jan-v1-4B
- Jan-v1-4B-GGUF: https://huggingface.co/janhq/Jan-v1-4B-GGUF
@ai_machinelearning_big_data
#ai#ml#local#Qwen#Jan
#FitLocal#Find#Local#Trainers
Join the FitLocal: Find Local Trainers beta on ✈️#TestFlight
🔗 Link: https://testflight.apple.com/join/tJE8FBcS
Shared by Dimitri
#go#cli#database#database_management#dbms#environment#local#postgres#postgresql#supabase
Supabase CLI lets you run Supabase locally, manage database migrations, deploy functions, generate types from your schema, and make secure API calls. Install easily via npm (`npm i supabase --save-dev`), Homebrew, Scoop, or binaries for any OS, then run `supabase init` and `supabase start` to launch your full stack with local URLs and keys. This benefits you by speeding up development, testing changes offline without cloud costs, ensuring type safety, and simplifying CI/CD for reliable deploys.
https://github.com/supabase/cli