Qwen3-VL-8B推理结果为空
123456789101112131415161718192021222324252627python -m vllm.entrypoints.openai.api_server \--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \--dtype bfloat16 \--gpu-memory-utilization 0.9 \--max-model-len 8192 \--max_num_batched_tokens 8192--host 0.0.0.0 \--port 8000 \--compilation-config '{"cudagraph_capture_sizes": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]}'curl -X POST http://localhost:8000/v1/chat/completions \ -H "Content-Type: app...
Qwen3-VL-8B在910B4推理调用超过上下文长度的图片报错
12345678# localscp -P 8333 images.zip root@139.9.155.20:/media/# remotesudo apt updatesudo apt install -y unzipcd /mediaunzip images.zip 准备图片： 12345678910111213141516#!/usr/bin/env bashimage_base64=$(base64 -w 0 /media/b0.jpg)cat > /media/image_request.json <<EOF{ "model": "/root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct", "messages": [ {"role": "system", "content": "You are a helpfu...
Qwen3-Omni没有vllm_config属性
1234class Qwen3OmniMoeThinkerForConditionalGeneration: def __init__(...): self.vllm_config = vllm_config
Qwen2.5-VL_RoPE计算流程详解
Qwen2.5-VisionTransformer 中 RoPE cos/sin 的计算流程整个流程从 forward(x, grid_thw) 开始，分为以下几个阶段： 第一步：初始化时预计算 cos/sin 缓存qwen2_5_vl.py:608-612 123456self.rotary_pos_emb = get_rope( head_size=head_dim, max_position=8192, is_neox_style=True, rope_parameters={"partial_rotary_factor": 0.5},) get_rope 最终创建一个 RotaryEmbedding 对象，关键参数： rotary_dim = head_dim * 0.5 → 只使用一半的 head 维度做旋转 在 _compute_cos_sin_cache 中(base.py:83-92)： 12345# inv_freq 形状: [rotary_dim // 2]inv_fr...
Qwen2.5-VL_1
Qwen2.5-VL 差异对比Qwen2_5_VLForConditionalGeneration__init__()123456789101112131415161718192021222324252627282930# vllmself.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"if multimodal_config.get_limit_per_prompt( "image") or multimodal_config.get_limit_per_prompt("video"): attn_backend_override = ( multimodal_config.mm_encoder_attn_backend if multimodal_config is not None else None ) self.visual = Qwen2_5_VisionTr...
Qwen2.5-VL
Qwen2.5-VL12Layer:- Qwen 方法 = vLLM 算子 LayersQwen2_5_VisionTransformer: patch_embed = Qwen2_5_VisionPatchEmbed rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding blocks = Qwen2_5_VisionBlock * layer_num merger = Qwen2_5_VisionPatchMerger Qwen2_5_VisionPatchEmbed: proj = nn.Conv3d Qwen2_5_VisionRotaryEmbedding: Qwen2_5_VisionBlock: norm1 = RMSNorm attn = Qwen2_5_VisionAttention norm2 = RMSNorm mlp = Qwen2_5_VisionMLP Qwen2_5_VisionAttention: ...
Qwen2-VL精度问题
Qwen2-VL 精度问题12345678------------------------------ Captured log call -------------------------------WARNING transformers.models.auto.image_processing_auto:logging.py:328 The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that t...
Qwen2-VL报错
1234apply_token_matchesapply_text_matchesTypeError: can't convert npu:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
Qwen2-VL
Qwen2-VLLayers Qwen2VisionMLP: fc1 = ColumnParallelLinear act = QuickGELU fc2 = RowParallelLinear Qwen2VisionAttention: qkv = ColumnParallelLinear proj = RowParallelLinear rotary_pos_emb attention 3 types Qwen2VisionBlock: norm1 = norm_layer norm2 = norm_layer attn = Qwen2VisionAttention mlp = Qwen2VisionMLP Qwen2VisionPatchEmbed: proj = nn.Conv3d Qwen2VisionPatchMerger: ln_q = norm_layer mlp = ColumnParallelLinear, nn.G...
Python异步编程概念与用法详解
我来详细解释Python异步编程中的核心概念，让你彻底理解async/await的工作原理。 1. 基本概念asyncasync关键字用于定义异步函数（协程函数）： 12345async def my_function(): return "Hello"# 调用异步函数会返回一个协程对象，而不是直接执行coroutine = my_function() awaitawait用于挂起当前协程，等待另一个协程完成： 123async def main(): result = await my_function() # 等待my_function完成 print(result) 2. Future对象Future代表一个异步操作的最终结果： 123456789101112131415import asyncioasync def example_future(): # 创建Future对象 future = asyncio.Future() # 设置结果 future.set_result("...