mit-han-lab / tinychat-tutorial
☆61Updated 4 months ago
Alternatives and similar repositories for tinychat-tutorial:
Users that are interested in tinychat-tutorial are comparing it to the libraries listed below
- Implement Flash Attention using Cute.☆74Updated 3 months ago
- llama INT4 cuda inference with AWQ☆53Updated 2 months ago
- ☆159Updated last year
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆59Updated 2 weeks ago
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆35Updated 2 weeks ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆89Updated 3 weeks ago
- Quantized Attention on GPU☆45Updated 4 months ago
- ☆88Updated 6 months ago
- ☆74Updated 3 months ago
- SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models☆17Updated 5 months ago
- The official PyTorch implementation of the NeurIPS2022 (spotlight) paper, Outlier Suppression: Pushing the Limit of Low-bit Transformer L…☆48Updated 2 years ago
- ☆64Updated 2 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆101Updated 8 months ago
- Official PyTorch implementation of FlatQuant: Flatness Matters for LLM Quantization☆110Updated 2 months ago
- ☆46Updated 2 months ago
- 📚FFPA(Split-D): Yet another Faster Flash Prefill Attention with O(1) GPU SRAM complexity for headdim > 256, ~2x↑🎉vs SDPA EA.☆147Updated last week
- [ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.☆107Updated 10 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆106Updated 6 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆60Updated 7 months ago
- ☆141Updated 2 years ago
- ☆29Updated 11 months ago
- An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization☆121Updated last month
- PyTorch extension for emulating FP8 data formats on standard FP32 Xeon/GPU hardware.☆107Updated 3 months ago
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆108Updated last week
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆35Updated 3 weeks ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆57Updated 6 months ago
- Examples of CUDA implementations by Cutlass CuTe☆145Updated last month
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing. By pro…☆68Updated this week
- LLM Inference with Microscaling Format☆20Updated 4 months ago