aju22 / LLaMA2
This repository contains an implementation of the LLaMA 2 (Large Language Model Meta AI) model, a Generative Pretrained Transformer (GPT) variant. The implementation focuses on the model architecture and the inference process. The code is restructured and heavily commented to facilitate easy understanding of the key parts of the architecture.
☆64Updated last year
Alternatives and similar repositories for LLaMA2:
Users that are interested in LLaMA2 are comparing it to the libraries listed below
- ☆195Updated 4 months ago
- Official PyTorch implementation of QA-LoRA☆131Updated last year
- ☆220Updated 9 months ago
- Simple implementation of Speculative Sampling in NumPy for GPT-2.☆92Updated last year
- Implementation of Speculative Sampling as described in "Accelerating Large Language Model Decoding with Speculative Sampling" by Deepmind☆91Updated last year
- ☆145Updated last year
- A repository dedicated to evaluating the performance of quantizied LLaMA3 using various quantization methods..☆180Updated 2 months ago
- [NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization☆338Updated 7 months ago
- Explorations into some recent techniques surrounding speculative decoding☆252Updated 3 months ago
- Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients.☆195Updated 8 months ago
- Low-bit optimizers for PyTorch☆125Updated last year
- ☆125Updated last year
- Code repo for the paper "LLM-QAT Data-Free Quantization Aware Training for Large Language Models"☆278Updated last month
- Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models☆228Updated 11 months ago
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆158Updated 8 months ago
- Prune transformer layers☆68Updated 10 months ago
- A family of compressed models obtained via pruning and knowledge distillation☆331Updated 4 months ago
- Code for studying the super weight in LLM☆94Updated 4 months ago
- Unofficial implementation for the paper "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆155Updated 9 months ago
- Official PyTorch implementation of DistiLLM: Towards Streamlined Distillation for Large Language Models (ICML 2024)☆203Updated 3 weeks ago
- The official implementation of the paper "What Matters in Transformers? Not All Attention is Needed".☆165Updated last week
- ring-attention experiments☆129Updated 5 months ago
- PB-LLM: Partially Binarized Large Language Models☆152Updated last year
- The official implementation of the EMNLP 2023 paper LLM-FP4☆193Updated last year
- Training code for Baby-Llama, our submission to the strict-small track of the BabyLM challenge.☆79Updated last year
- [MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving☆303Updated 9 months ago
- Positional Skip-wise Training for Efficient Context Window Extension of LLMs to Extremely Length (ICLR 2024)☆205Updated 10 months ago
- ☆122Updated last month
- Block Transformer: Global-to-Local Language Modeling for Fast Inference (NeurIPS 2024)☆150Updated 3 months ago
- ☆234Updated 11 months ago