LLaMA3 (Large Language Model by META AI) is a leading-edge large language model that excels in AI technology. 🌟 This repository📁 is intended to provide information necessary to kick-start various projects🚀 using LLaMA3.
| Name | Description | Link |
|---|---|---|
| Grok | High-performance AI Chip enabling LLaMA3 inference and API calls | Grok 🌐 |
| AWS | Bedrock support for LLaMA at AWS, currently only Llama2 available | AWS 🌐 |
| Azure | Support for 8B/70B models on Microsoft Azure, searchable via Azure Marketplace | Azure 🌐 |
| GCP | Google Cloud Vertax AI support for LLaMA3 | GCP 🌐 |
| together.ai | Support for Llama2, CodeLlama, Llama3 8b/70b instances | together.ai 🌐 |
| replicate | Llama3 API support (Node.js, Python, HTTP) | replicate 🌐 |
| llama AI | Support for Llama3 8B/70B, supports other OpenLLMs | llama AI 🌐 |
| aimlapi | Supports various openLLMs as APIs | AI/ML API |
| Nvidia API | Multiple OpenLLM models available Nvidia devloper | llama AI 🌐 |
| Meta AI(github) | Connect to Meta AI api | MetaAI 🌐 |
| Platform Name | Description | Link |
|---|---|---|
| HuggingFace | Llama 8B model | Link 🌐 |
| HuggingFace | Llama 70B model | Link 🌐 |
| HuggingFace | Llama 8B Instruct model | Link 🌐 |
| HuggingFace | Llama 70B Instruct model | Link 🌐 |
| HuggingFace | Llama Guard-2-8B(policy model) | Link 🌐 |
| HuggingFace | Llama 3 70B - FP8(friendliAI) | Link 🌐 |
| HuggingFace | Llama 3 70B Instruct - FP8(friendliAI) | Link 🌐 |
| HuggingFace | Llama 3 8B - FP8(friendliAI) | Link 🌐 |
| HuggingFace | Llama 3 8B Instruct - FP8(friendliAI) | Link 🌐 |
| HuggingFace | Llama 8B KO (made beomi) | Link 🌐 |
| Ollama | Support for various lightweight Llama3 models | Link 🌐 |
| Name | Description | Link |
|---|---|---|
| gradientai/Llama-3-8B-Instruct-Gradient-1048k | 1M Long Context | Link 🌐 |
| Trelis/Meta-Llama-3-70B-Instruct-function-calling | function calling | Link 🌐 |
| Trelis/Meta-Llama-3-8B-Instruct-function-calling | function calling | Link 🌐 |
| cognitivecomputations/dolphin-2.9-llama3-8b | Uncensored fine-tuning | Link 🌐 |
| McGill-NLP/Llama-3-8B-Web | Zero-shot internet link selection capability | Link 🌐 |
| teddylee777/Llama-3-Open-Ko-8B-Instruct-preview-gguf | Korean quantizatied GGUF model for ollama use | Link 🌐 |
| beomi/Llama-3-Open-Ko-8B-Instruct-preview | Korean model trained with the Chat vector method | Link 🌐 |
| Name | Link |
|---|---|
| HuggingChat | Link 🌐 |
| Groq | Link 🌐 |
| together.ai | Link 🌐 |
| replicate Llama chat(local) | Link 🌐 |
| perplexity.ai(lightweight model) | Link 🌐 |
| openrouter.ai | Link 🌐 |
| MetaAI (Not available in Korea) | Link 🌐 |
| Morphic(multimodal offerings) | Link 🌐 |
| Nvidia AI | [Link 🌐 |
| Name | Type | Link |
|---|---|---|
| Langchain | RAG | Link 🌐 |
| llamaindex | RAG | Link 🌐 |
| llama.cpp | convert | Link 🌐 |
| Name | Link |
|---|---|
| Meta | Link 🌐 |
| torchrune | Link 🌐 |
| LLaMAFactory | Link 🌐 |
| axolotl | Link 🌐 |
| Information | Link |
|---|---|
| Prompt Engineering Guide | Link 🌐 |
| Using llama3 with WEB UI | Link 🌐 |
| API with Ollama, LangChain and ChromaDB with Flask API and PDF upload | Link 🌐 |
| Guide for tuning and inference with Llama on MacBook | Link 🌐 |
| Fine-tune Llama 3 with ORPO | Link 🌐 |
| Qlora_aplaca_llama3 finetune | Link 🌐 |
| fully local RAG agents with LLama3 | Link 🌐 |
| RAG Chatbot LLama3(HF) | Link 🌐 |
| llama index RAG llama3 | Link 🌐 |
| ollama RAG + UI(Gradio) | Link 🌐 |
| LangGraph + Llama3 | Link 🌐 |
| RAG(Re-Ranking) | Link 🌐 |
| Information | Link |
|---|---|
| HuggingFaceFW/fineweb | Link 🌐 |
| mlabonne/orpo-dpo-mix-40k | Link 🌐 |
| Information | Link |
|---|---|
| FSDP+QLORA finetunning | Link 🌐 |
| Category | M3 Max | M1 Pro | RTX 4090 |
|---|---|---|---|
| CPU Cores | 16 cores | 10 cores | 16 cores AMD |
| Memory | 128GB | 16GB /32GB | 32GB |
| GPU Memory | 16 core CPU & 40 core GPU, 400GB/s memory bandwidth | 10 core CPU(8 performance cores & 2 efficiency cores) 16 core GPU 200GB/s memory bandwidth | 24GB |
| Model 7B | - Performs well on all computers | - Performs well on all computers | - Performs well on all computers, similar performance to M3 Max |
| Model 13B | - Good performance | - Third best performance | - Best performance |
| Model 70B | - Runs quickly, utilizing 128GB memory | - Lacks memory at 16GB, prone to crashes and reboots | - Cannot run on GPU, very slow on CPU |
| Lightening | Not necessary with sufficient memory | Should be considered | Quantization compromises necessary |
| Power Consumption | 65W | 250-300W | |
| Value for Money | Excellent ($4600) | Relatively low ($6000 for A6000 GPU) |
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