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如何做公司的网站,网站建设关键词,个人博客网站开发毕业设计,网站界面设计的要求vLLM 是一款专为大语言模型推理加速而设计的框架#xff0c;实现了 KV 缓存内存几乎零浪费#xff0c;解决了内存管理瓶颈问题。 更多 vLLM 中文文档及教程可访问 →vllm.hyper.ai/ *在线运行 vLLM 入门教程#xff1a;零基础分步指南 源码 examples/offline_inference/p…vLLM 是一款专为大语言模型推理加速而设计的框架实现了 KV 缓存内存几乎零浪费解决了内存管理瓶颈问题。更多 vLLM 中文文档及教程可访问 →vllm.hyper.ai/*在线运行 vLLM 入门教程零基础分步指南源码 examples/offline_inference/prefix_caching.py# SPDX-License-Identifier: Apache-2.0 from vllm import LLM, SamplingParams from vllm.distributed import cleanup_dist_env_and_memory # 注意:这只是一个正在运行的示例。用于基准测试 # 请参阅基准 benchmarks/benchmark_prefix_caching.py # 常见前缀。 prefix ( You are an expert school principal, skilled in effectively managing faculty and staff. Draft 10-15 questions for a potential first grade Head Teacher for my K-12, all-girls, independent school that emphasizes community, joyful discovery, and life-long learning. The candidate is coming in for a first-round panel interview for a 8th grade Math teaching role. They have 5 years of previous teaching experience as an assistant teacher at a co-ed, public school with experience in middle school math teaching. Based on these information, fulfill the following paragraph: ) # 样本提示。 prompts [ Hello, my name is, The president of the United States is, The capital of France is, The future of AI is, ] generating_prompts [prefix prompt for prompt in prompts] # 创建一个采样参数对象。 sampling_params SamplingParams(temperature0.0) # 创建一个没有前缀缓存的 LLM 作为基线。 regular_llm LLM(modelfacebook/opt-125m, gpu_memory_utilization0.4) print(Results without enable_prefix_caching) # 从提示中生成文本。输出是 RequestOutput 对象的包含提示生成的文本和其他信息的对象列表。 outputs regular_llm.generate(generating_prompts, sampling_params) regular_generated_texts [] # 打印输出。 for output in outputs: prompt output.prompt generated_text output.outputs[0].text regular_generated_texts.append(generated_text) print(fPrompt: {prompt!r}, Generated text: {generated_text!r}) print(- * 80) # 破坏 LLM 对象并释放 GPU 内存。 del regular_llm cleanup_dist_env_and_memory() # 使用启用前缀缓存创建一个 LLM。 prefix_cached_llm LLM(modelfacebook/opt-125m, enable_prefix_cachingTrue, gpu_memory_utilization0.4) # 预热以便计算共享的提示 KV 缓存。 prefix_cached_llm.generate(generating_prompts[0], sampling_params) # 使用前缀缓存生成。 outputs prefix_cached_llm.generate(generating_prompts, sampling_params) print(Results with enable_prefix_caching) cached_generated_texts [] # 打印输出。您应该看到与以前相同的输出。 for output in outputs: prompt output.prompt generated_text output.outputs[0].text cached_generated_texts.append(generated_text) print(fPrompt: {prompt!r}, Generated text: {generated_text!r}) print(- * 80) # 比较结果并显示加速 generated_same all([ regular_generated_texts[i] cached_generated_texts[i] for i in range(len(prompts)) ]) print(fGenerated answers are the same: {generated_same})