164 lines
3.9 KiB
Plaintext
164 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# Querying Qwen-VL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import nest_asyncio\n",
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"\n",
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"nest_asyncio.apply() # Run this first.\n",
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"\n",
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"model_path = \"Qwen/Qwen2.5-VL-3B-Instruct\"\n",
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"chat_template = \"qwen2-vl\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Lets create a prompt.\n",
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"\n",
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"from io import BytesIO\n",
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"import requests\n",
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"from PIL import Image\n",
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"\n",
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"from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest\n",
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"from sglang.srt.conversation import chat_templates\n",
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"\n",
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"image = Image.open(\n",
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" BytesIO(\n",
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" requests.get(\n",
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" \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n",
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" ).content\n",
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" )\n",
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")\n",
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"\n",
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"conv = chat_templates[chat_template].copy()\n",
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"conv.append_message(conv.roles[0], f\"What's shown here: {conv.image_token}?\")\n",
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"conv.append_message(conv.roles[1], \"\")\n",
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"conv.image_data = [image]\n",
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"\n",
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"print(conv.get_prompt())\n",
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"image"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3",
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"metadata": {},
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"source": [
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"## Query via the offline Engine API"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sglang import Engine\n",
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"\n",
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"llm = Engine(\n",
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" model_path=model_path, chat_template=chat_template, mem_fraction_static=0.8\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"out = llm.generate(prompt=conv.get_prompt(), image_data=[image])\n",
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"print(out[\"text\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6",
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"metadata": {},
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"source": [
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"## Query via the offline Engine API, but send precomputed embeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compute the image embeddings using Huggingface.\n",
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"\n",
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"from transformers import AutoProcessor\n",
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"from transformers import Qwen2_5_VLForConditionalGeneration\n",
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"\n",
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"processor = AutoProcessor.from_pretrained(model_path, use_fast=True)\n",
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"vision = (\n",
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" Qwen2_5_VLForConditionalGeneration.from_pretrained(model_path).eval().visual.cuda()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8",
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"metadata": {},
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"outputs": [],
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"source": [
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"processed_prompt = processor(\n",
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" images=[image], text=conv.get_prompt(), return_tensors=\"pt\"\n",
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")\n",
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"input_ids = processed_prompt[\"input_ids\"][0].detach().cpu().tolist()\n",
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"precomputed_features = vision(\n",
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" processed_prompt[\"pixel_values\"].cuda(), processed_prompt[\"image_grid_thw\"].cuda()\n",
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")\n",
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"\n",
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"mm_item = dict(\n",
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" modality=\"IMAGE\",\n",
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" image_grid_thw=processed_prompt[\"image_grid_thw\"],\n",
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" precomputed_features=precomputed_features,\n",
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")\n",
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"out = llm.generate(input_ids=input_ids, image_data=[mm_item])\n",
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"print(out[\"text\"])"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"custom_cell_magics": "kql",
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"encoding": "# -*- coding: utf-8 -*-"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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