#llama2 (15 Repositories)
Ranked open-source repositories tagged with #llama2, scored by pull request acceptance likelihood and maintainer engagement velocity.
13.5%
7.1h
15 repositories tagged #llama2
InternLM/lmdeploy
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
AI-Hypercomputer/maxtext
A simple, performant, and scalable Jax LLM!
open-compass/opencompass
OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
Mobile-Artificial-Intelligence/llama_sdk
lcpp is a dart implementation of llama.cpp used by the mobile artificial intelligence distribution (maid)
cgbur/llama2.zig
Inference Llama 2 in one file of pure Zig
WisconsinAIVision/ViP-LLaVA
[CVPR2024] ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
KolosalAI/Kolosal
Kolosal AI is an OpenSource and Lightweight alternative to LM Studio to run LLMs 100% offline on your device.
hpcaitech/SwiftInfer
Efficient AI Inference & Serving
KolosalAI/kolosal-cli
Super lightweight Ollama + Qwen Code alternative to run Llama 3.3, DeepSeek-R1, Phi-4, Gemma 3, Mistral Small 3.1 and other large language models.
princeton-nlp/LLM-Shearing
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
seanoliver/audioflare
An all-in-one AI audio playground using Cloudflare AI Workers to transcribe, analyze, summarize, and translate any audio file.
gbaptista/ollama-ai
A Ruby gem for interacting with Ollama's API that allows you to run open source AI LLMs (Large Language Models) locally.
meta-llama/llama-cookbook
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Dicklesworthstone/llm_aided_ocr
Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs
ashishpatel26/LLM-Finetuning
LLM Finetuning with peft