所以我们需要大于14GB的显存,选择30%A100*1(24GB显存容量),后选择立即创建,等状态栏变成运行中,点击进入开发机,我们即可开始部署。 在终端中,让我们输入以下指令,来创建一个名为lmdeploy的conda环境,python版本为3.10,创建成功后激活环境并安装0.5.3版本的lmdeploy及相关包。 conda create -n lmdeploy python=3.10...
.github autotest benchmark builder cmake docker docs en zh_cn _static advance api benchmark get_started inference llm api_server.md api_server_lora.md api_server_tools.md codellama.md gradio.md pipeline.md proxy_server.md multi_modal quantization supported_models .readthedocs.yaml Makefile ...
13Branches48Tags Code Folders and files Name Last commit message Last commit date Latest commit grimoire Warmup deepgemm (#3387) Apr 9, 2025 05914be·Apr 9, 2025 History 1,229 Commits .github autotest benchmark builder cmake docker
.github autotest benchmark builder cmake docker docs en _static advance api benchmark get_started inference llm multi_modal quantization kv_quant.md w4a16.md w8a8.md supported_models .readthedocs.yaml Makefile conf.py faq.md index.rst make.bat zh_cn examples k8s lmdeploy requirements resources...
.github 3rdparty autotest benchmark builder cmake docker docs en _static advance api benchmark inference quantization serving supported_models codellama.md supported_models.md .readthedocs.yaml Makefile build.md conf.py faq.md get_started.md index.rst make.bat switch_language.md zh_cn examples ...
.github 3rdparty autotest benchmark builder cmake docker docs en _static advance chat_template.md debug_turbomind.md long_context.md pytorch_new_model.md api benchmark inference quantization serving supported_models .readthedocs.yaml Makefile build.md conf.py faq.md get_started.md index.rst make...
.github 3rdparty autotest benchmark builder cmake docker docs en _static advance api benchmark inference load_hf.md pipeline.md pytorch.md turbomind.md turbomind_config.md vl_pipeline.md multi_modal quantization serving supported_models .readthedocs.yaml Makefile build.md conf.py faq.md get_sta...
vl import load_image pipe = pipeline('OpenGVLab/InternVL2-8B') image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg') response = pipe(('describe this image', image)) print(response) In VLM pipeline, the default image processing batch size...
If you have requests to build lmdeploy from source, please clone lmdeploy repository from GitHub, and follow instructions in next sections git clone --depth=1 https://github.com/InternLM/lmdeploy Build in Docker (recommended) We highly advise using the provided docker image for lmdeploy build...
vl import load_image pipe = pipeline('liuhaotian/llava-v1.6-vicuna-7b', backend_config=TurbomindEngineConfig(session_len=8192)) image_urls=[ 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg', 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/...