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Bruno Scaglione's Projects

lineapy icon lineapy

Move fast from data science prototype to pipeline. Capture, analyze, and transform messy notebooks into data pipelines with just two lines of code.

lit icon lit

The Language Interpretability Tool: Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.

litefs icon litefs

FUSE-based file system for replicating SQLite databases across a cluster of machines

litellm icon litellm

Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)

litestar icon litestar

Production-ready, Light, Flexible and Extensible ASGI API framework | Effortlessly Build Performant APIs

litex icon litex

Build your hardware, easily!

litgpt icon litgpt

Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.

litmus icon litmus

Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub (https://hub.litmuschaos.io). Community notes is at https://hackmd.io/a4Zu_sH4TZGeih-xCimi3Q

littlejs icon littlejs

LittleJS is a lightweight HTML5 game engine with many features and no depenencies. 🚂 Choo-Choo!

livekit icon livekit

End-to-end stack for WebRTC. SFU media server and SDKs.

llama icon llama

Inference code for LLaMA models

llama-factory icon llama-factory

Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, ChatGLM2)

llama-fs icon llama-fs

A self-organizing file system with llama 3

llama3 icon llama3

The official Meta Llama 3 GitHub site

llamafile icon llamafile

Distribute and run LLMs with a single file.

llm-awq icon llm-awq

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

llm-course icon llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

llm-finetuning-hub icon llm-finetuning-hub

Repository that contains LLM fine-tuning and deployment scripts along with our research findings.

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