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README.md

@@ -92,95 +92,14 @@ conda create -n llava_vllm python=3.10
 
 ### 推理
 
-```
-import argparse
-import os
-
-import torch
-from PIL import Image
-
-from vllm import LLM, SamplingParams
-from vllm.multimodal.image import ImageFeatureData, ImagePixelData
-
-
-def run_llava_pixel_values(*, disable_image_processor: bool = False):
-    llm = LLM(
-        model="llava/llava-1.5-7b-hf",
-        image_input_type="pixel_values",
-        image_token_id=32000,
-        image_input_shape="1,3,336,336",
-        image_feature_size=576,
-        disable_image_processor=disable_image_processor,
-    )
-
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
-
-    if disable_image_processor:
-        image = torch.load("images/stop_sign_pixel_values.pt")
-    else:
-        image = Image.open("/images/stop_sign.jpg") #图片位置
-
-    outputs = llm.generate({
-        "prompt": prompt,
-        "multi_modal_data": ImagePixelData(image),
-    })
-
-    for o in outputs:
-        generated_text = o.outputs[0].text
-        print(generated_text)
-
-
-def run_llava_image_features():
-    llm = LLM(
-        model="llava/llava-1.5-7b-hf",
-        image_input_type="image_features",
-        image_token_id=32000,
-        image_input_shape="1,576,1024",
-        image_feature_size=576,
-    )
-
-    prompt = "<image>" * 576 + (
-        "\nUSER: What is the content of this image?\nASSISTANT:")
-
-    image: torch.Tensor = torch.load("images/stop_sign_image_features.pt")
-
-    sampling_params = SamplingParams(temperature=0, max_tokens=64)
-    outputs = llm.generate({
-            "prompt": prompt,
-            "multi_modal_data": ImageFeatureData(image),
-        }, sampling_params=sampling_params)
-    for o in outputs:
-        generated_text = o.outputs[0].text
-        print(generated_text)
-
-
-def main(args):
-    if args.type == "pixel_values":
-        run_llava_pixel_values()
-    else:
-        run_llava_image_features()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Demo on Llava")
-    parser.add_argument("--type",
-                        type=str,
-                        choices=["pixel_values", "image_features"],
-                        default="pixel_values",
-                        help="image input type")
-    args = parser.parse_args()
-   
-    main(args)
-
-```
-
 ```bash
-python examples/offline_inference.py
+python examples/llava_example.py
 ```
 
-其中，`prompts`为提示词；`temperature`为控制采样随机性的值，值越小模型生成越确定，值变高模型生成更随机，0表示贪婪采样，默认为1；`max_tokens=16`为生成长度，默认为1；
-`model`为模型路径；`tensor_parallel_size=1`为使用卡数，默认为1；`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理,`quantization="gptq"`为使用gptq量化进行推理,需下载以上GPTQ模型。`quantization="awq"`为使用awq量化进行推理,需下载以上AWQ模型。
+为了确保源码能够正常运行，还需要进行以下调整：
+
+* **去除了AWS CLI 下载逻辑** ：
+* **移除对 `subprocess` 和 `os` 模块的部分依赖**
 
 ### 离线批量推理性能测试