vllm 支持Qwen 系列模型的推理,支持 AWQ 量化方式。 vllm-gptq 为vllm 添加了 GPTQ 支持,目前采用了 exllamav2 的 gptq kernel text-generation-inference 没有千问的支持 压测方法 benchmark.py 为主要的压测脚本实现,实现了一个 naive 的 asyncio + ProcessPoolExec
FlexLLMGen can be integrated intoHELM, a language model benchmark framework, as its execution backend. You can use the commands below to run a Massive Multitask Language Understanding (MMLU)scenariowith a single T4 (16GB) GPU and 200GB of DRAM. ...
Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA) Vulkan and SYCL backend support CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity ...
This repo hosts code for vLLM CI & Performance Benchmark infrastructure. HCL 11 27 0 6 Updated Jun 5, 2025 vllm-ascend Public Community maintained hardware plugin for vLLM on Ascend Python 717 Apache-2.0 179 147 (4 issues need help) 63 Updated Jun 5, 2025 production-stack Public...
原名CLiB,现已更名为ReLE (Really Reliable Live Evaluation for LLM) 目前已囊括237个大模型,覆盖chatgpt、gpt-4o、o3-mini、谷歌gemini-2.5、Claude3.5、智谱GLM-Zero、文心一言、qwen-max、百川、讯飞星火、商汤senseChat、minimax等商用模型,以及DeepSeek-R1、qwq-32b、deepseek-v3、qwen3、llama4、phi-4、gl...
train/benchmarking- profile training throughput and MFU inference/- convert models to HuggingFace or ONNX format, and generate responses inference/benchmarking- profile inference latency and throughput eval/- evaluate LLMs on academic (or custom) in-context-learning tasks ...
It also includes a backend for integration with the NVIDIA Triton Inference Server; a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using Tensor Parallelism ...
DeepSpeed on AzureML Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference [slides] Community Tutorials DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim) Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher) Ultimate Gui...
Running Benchmarks To run benchmarks, use the provided Python script with the path to your YAML configuration: python main.py --config configs/llama3_8b_tp_1.yaml The script will parse the YAML file, start the Docker container, and run the benchmarks. The results will be saved in a ...
2024-11-11, 🎉🎉the paper on model editing for LLMs4Code, "Model Editing for LLMs4Code: How Far are We?", has been accepted by ICSE 2025! This work proposes a benchmark for LLMs4Code editing, CLMEEval, which is built upon EasyEdit! 2024-11-09, we fixed a bug regarding the ...