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"output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab.zip -> /content/qwen-test-runner\n", "contents: ['LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260627T205034_527809Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "processor_config.json: 0%| | 0.00/1.19k [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n" ] }, { "cell_type": "code", "source": [ "# ==========================================================================================\n", "# qwen-vlmbench — Colab cells for the RTX 6000 Pro (96GB)\n", "#\n", "# HOW TO USE\n", "# 1. Drag qwen_vlmbench_colab.zip into the Colab file browser (it lands in /content/).\n", "# 2. Paste CELL 1 into a Colab cell and run it. It unzips, installs, and VERIFIES the\n", "# qwen_test_runner + vision harness with NO model download (fast, ~1-2 min).\n", "# 3. (Optional) Paste CELL 2 into a SEPARATE cell to load a real Qwen VLM and run the\n", "# full pipeline on it. This one downloads weights + transformers@main (heavier).\n", "#\n", "# These are independent cells — copy each block into its own Colab cell.\n", "# ==========================================================================================\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 1 — ESTABLISH THE SYSTEM (install + verify, no model download)\n", "# ==========================================================================================\n", "import glob, os, subprocess, zipfile\n", "\n", "def sh(cmd):\n", " print(\"\\n$ \" + cmd)\n", " subprocess.run(cmd, shell=True)\n", "\n", "# 1) locate + unzip the uploaded package\n", "zips = glob.glob(\"/content/qwen_vlmbench_colab*.zip\") + glob.glob(\"qwen_vlmbench_colab*.zip\")\n", "assert zips, \"Upload qwen_vlmbench_colab.zip to /content first (drag it into the file browser).\"\n", "ZIP = sorted(zips)[-1]\n", "with zipfile.ZipFile(ZIP) as z:\n", " z.extractall(\"/content\")\n", "PKG = \"/content/qwen-test-runner\"\n", "assert os.path.isdir(PKG), f\"expected {PKG} after unzip; got {os.listdir('/content')}\"\n", "print(\"unzipped:\", ZIP, \"->\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 3 — NATIVE-JSON CAPABILITY PROBE across the small-model ladder\n", "# Answers the investigation's headline question: which base models emit clean per-task\n", "# JSON *natively* (native_capable) vs need repair (finetune_candidate)? Also validates the\n", "# throughput fix (causal-conv1d fast path). Uses synthetic probe images, so it needs NO\n", "# ground-truth download — it measures JSON shape + robustness + speed, not accuracy.\n", "# Output is a compact table to paste back.\n", "#\n", "# Edit MODELS to add/remove rungs. Small first: 0.8B captioner vs 0.8B base shows what the\n", "# LoRA did to native JSON shape; 2B/4B/9B show whether bigger base models are clean natively.\n", "# ==========================================================================================\n", "import subprocess, time\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps (idempotent) + the fast-path libs that were MISSING in the CELL-2 run (8 tok/s).\n", "# On Colab Linux these build fine (unlike the Windows dev box).\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\")\n", "sh(\"pip -q install flash-linear-attention || echo 'fla install failed — ok'\")\n", "sh(\"pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\")\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit this list to choose the ladder rungs to probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"] # constrained needs xgrammar (falls back if missing)\n", "\n", "# ---- synthetic probe images (offline, deterministic) ----\n", "def _img_classification():\n", " return Image.new(\"RGB\", (256, 256), (40, 90, 160)) # a solid blue image\n", "\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)) # red box\n", " d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)) # blue box\n", " return im\n", "\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.text((40, 45), \"STOP\", fill=(0, 0, 0))\n", " return im\n", "\n", "PROBE_IMAGES = {\n", " \"image_classification\": _img_classification(),\n", " \"bbox_grounding\": _img_detection(),\n", " \"ocr_text\": _img_ocr(),\n", "}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, est {spec.est_vram_gb('bf16'):.1f}GB bf16) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(f\" LOAD FAILED: {type(e).__name__}: {str(e)[:200]}\")\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat)\n", " img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " snippet = res.raw_text.replace(\"\\n\", \" \")[:48]\n", " rows.append((mkey, cat, res.backend + \"/\" + mode,\n", " \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\",\n", " \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", snippet))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "# ---- compact, paste-friendly table ----\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ NATIVE-JSON CAPABILITY PROBE ================\\n\")\n", "print(line(hdr))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(line(r))\n", "\n", "# per-model native verdict (json_mode only = no grammar crutch)\n", "print(\"\\n---- native bucket (json_mode: emits clean valid JSON on its own?) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " if not jm:\n", " continue\n", " valid = sum(1 for r in jm if r[3] == \"Y\")\n", " robust = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if robust == len(jm) else (\"finetune_candidate\" if valid else \"insufficient/needs-look\")\n", " print(f\" {mkey:30s} valid {valid}/{len(jm)} robust {robust}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole PROBE block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "f872063ef24b4dda9618dba77d49475b", "ef8441954a6f4b688fb54cc4ad12c41e", "e57950f8f44e493a8367836983451b6f", "21239c31e7cc4612b257c22ed8fcd9be", "0cb8c918d67245269ed4201e343e5d47", "e69d889be7784ddea66882a0f87a714d", "7f0e92da571b4fd5b7785d53eebeccd8", "8555b723d207478288d40b9dab18a3c3", "1fcd580b13c64d4899b373f97e3b11be", "328597b57af946a099712d1aedadf8a9", "f290c05b707648dca1228937fb046ed7", "d0d666092ca24dc581de1e5a1dfccc6a", "6dd70af877554635b6a707930305bae9", "cd592203ba4f4e01b33d015d306ca64e", "7844d9fb04f7411a898f084b4d64d177", "f7c409995efc4d77b7e0337b5061434c", "9f349ce9f7fa4569b039af7568f829be", 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"f4126f0253f44047a6b6721b883b3965" ] }, "id": "BNdrCb218l47", "outputId": "ff67ac97-880e-4ad8-bca0-a16ae85e62fc" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: /content/qwen_vlmbench_colab.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260627T211827_029194Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate\n", " qwen3.5-0.8b valid 1/3 robust 1/3 -> finetune_candidate\n", " qwen3.5-2b valid 3/3 robust 1/3 -> finetune_candidate\n", " qwen3.5-4b valid 2/3 robust 1/3 -> finetune_candidate\n", "\n", "Paste this whole PROBE block back.\n" ] } ] }, { "cell_type": "code", "source": [ "# ==========================================================================================\n", "# qwen-vlmbench — Colab cells for the RTX 6000 Pro (96GB)\n", "#\n", "# HOW TO USE\n", "# 1. Drag qwen_vlmbench_colab.zip into the Colab file browser (it lands in /content/).\n", "# 2. Paste CELL 1 into a Colab cell and run it. It unzips, installs, and VERIFIES the\n", "# qwen_test_runner + vision harness with NO model download (fast, ~1-2 min).\n", "# 3. (Optional) Paste CELL 2 into a SEPARATE cell to load a real Qwen VLM and run the\n", "# full pipeline on it. This one downloads weights + transformers@main (heavier).\n", "#\n", "# These are independent cells — copy each block into its own Colab cell.\n", "# ==========================================================================================\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 1 — ESTABLISH THE SYSTEM (install + verify, no model download)\n", "# ==========================================================================================\n", "import glob, os, subprocess, zipfile\n", "\n", "def sh(cmd):\n", " print(\"\\n$ \" + cmd)\n", " subprocess.run(cmd, shell=True)\n", "\n", "# 1) locate + unzip the uploaded package\n", "zips = glob.glob(\"/content/qwen_vlmbench_colab*.zip\") + glob.glob(\"qwen_vlmbench_colab*.zip\")\n", "assert zips, \"Upload qwen_vlmbench_colab.zip to /content first (drag it into the file browser).\"\n", "ZIP = sorted(zips)[-1]\n", "with zipfile.ZipFile(ZIP) as z:\n", " z.extractall(\"/content\")\n", "PKG = \"/content/qwen-test-runner\"\n", "assert os.path.isdir(PKG), f\"expected {PKG} after unzip; got {os.listdir('/content')}\"\n", "print(\"unzipped:\", ZIP, \"->\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 3 — NATIVE-JSON CAPABILITY PROBE across the small-model ladder\n", "# Answers the investigation's headline question: which base models emit clean per-task\n", "# JSON *natively* (native_capable) vs need repair (finetune_candidate)? Also validates the\n", "# throughput fix (causal-conv1d fast path). Uses synthetic probe images, so it needs NO\n", "# ground-truth download — it measures JSON shape + robustness + speed, not accuracy.\n", "# Output is a compact table to paste back.\n", "#\n", "# Edit MODELS to add/remove rungs. Small first: 0.8B captioner vs 0.8B base shows what the\n", "# LoRA did to native JSON shape; 2B/4B/9B show whether bigger base models are clean natively.\n", "# ==========================================================================================\n", "import subprocess, time\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps (idempotent) + the fast-path libs that were MISSING in the CELL-2 run (8 tok/s).\n", "# On Colab Linux these build fine (unlike the Windows dev box).\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\")\n", "sh(\"pip -q install flash-linear-attention || echo 'fla install failed — ok'\")\n", "sh(\"pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\")\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit this list to choose the ladder rungs to probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"] # constrained needs xgrammar (falls back if missing)\n", "\n", "# ---- synthetic probe images (offline, deterministic) ----\n", "def _img_classification():\n", " return Image.new(\"RGB\", (256, 256), (40, 90, 160)) # a solid blue image\n", "\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)) # red box\n", " d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)) # blue box\n", " return im\n", "\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.text((40, 45), \"STOP\", fill=(0, 0, 0))\n", " return im\n", "\n", "PROBE_IMAGES = {\n", " \"image_classification\": _img_classification(),\n", " \"bbox_grounding\": _img_detection(),\n", " \"ocr_text\": _img_ocr(),\n", "}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, est {spec.est_vram_gb('bf16'):.1f}GB bf16) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(f\" LOAD FAILED: {type(e).__name__}: {str(e)[:200]}\")\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat)\n", " img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " snippet = res.raw_text.replace(\"\\n\", \" \")[:48]\n", " rows.append((mkey, cat, res.backend + \"/\" + mode,\n", " \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\",\n", " \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", snippet))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "# ---- compact, paste-friendly table ----\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ NATIVE-JSON CAPABILITY PROBE ================\\n\")\n", "print(line(hdr))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(line(r))\n", "\n", "# per-model native verdict (json_mode only = no grammar crutch)\n", "print(\"\\n---- native bucket (json_mode: emits clean valid JSON on its own?) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " if not jm:\n", " continue\n", " valid = sum(1 for r in jm if r[3] == \"Y\")\n", " robust = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if robust == len(jm) else (\"finetune_candidate\" if valid else \"insufficient/needs-look\")\n", " print(f\" {mkey:30s} valid {valid}/{len(jm)} robust {robust}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole PROBE block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 4 — FIX: turn ON constrained decoding (xgrammar) + stronger anti-fence prompts,\n", "# then re-run the probe.\n", "#\n", "# IMPORTANT: run this on a FRESH kernel. First do Runtime -> Restart runtime (this keeps\n", "# the downloaded models on disk, so no re-download). xgrammar must be installed BEFORE\n", "# qwen_test_runner is imported, or its constrained backend stays off.\n", "#\n", "# What changes vs CELL 3:\n", "# * installs xgrammar FIRST and shows any error (CELL 3 swallowed it -> constrained was a no-op)\n", "# * patches the 3 pilot prompts to forbid ``` fences and show the exact schema + the\n", "# 'box' key (so json_mode \"native\" numbers improve; mirrors the v2 zip)\n", "# * silences the noisy [transformers] processor-kwargs warning\n", "# With xgrammar live, the 'constrained' rows should show backend=xgrammar and flip to valid+robust.\n", "# ==========================================================================================\n", "import subprocess\n", "\n", "# ---- 1. install deps + xgrammar BEFORE importing qwen_test_runner ----\n", "def sh(c):\n", " print(\"\\n$ \" + c); subprocess.run(c, shell=True)\n", "\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed'\")\n", "print(\"\\n=== installing xgrammar (FULL output so we can see any error) ===\")\n", "subprocess.run(\"pip install xgrammar\", shell=True)\n", "try:\n", " import xgrammar as _xgr\n", " print(\"\\nxgrammar import OK — version\", getattr(_xgr, \"__version__\", \"?\"))\n", " XGRAMMAR = True\n", "except Exception as e:\n", " print(\"\\nxgrammar import FAILED:\", type(e).__name__, \"::\", str(e)[:400])\n", " XGRAMMAR = False\n", "\n", "# ---- 2. silence the benign [transformers] processor-kwargs spam ----\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers\n", " transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "# ---- 3. import the harness (AFTER xgrammar so its backend is detected) ----\n", "import time\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "# ---- 4. patch in the stronger anti-fence prompts (mirrors the v2 zip) ----\n", "_V2_PROMPTS = {\n", " \"image_classification\":\n", " \"You are an image classifier. Identify the single most prominent object or scene \"\n", " \"category in the image. Output ONLY a raw JSON object and NOTHING else — no prose, \"\n", " \"no explanation, and NO markdown code fences (do not wrap it in ```). It must match \"\n", " 'this shape exactly:\\n{\"label\": \"\", \"confidence\": , '\n", " '\"top5\": [{\"label\": \"\", \"score\": }]}',\n", " \"bbox_grounding\":\n", " \"You are an object detector. Find every distinct object in the image. Output ONLY a \"\n", " \"raw JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it \"\n", " 'in ```). It must match this shape exactly:\\n{\"detections\": [{\"label\": \"\", '\n", " '\"box\": [x1, y1, x2, y2], \"score\": }], \"count\": }\\n{coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with exactly four numbers [x1, y1, x2, y2].',\n", " \"ocr_text\":\n", " \"You are an OCR engine. Transcribe all readable text in the image. Output ONLY a raw \"\n", " \"JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it in \"\n", " '```). It must match this shape exactly:\\n{\"full_text\": \"\", '\n", " '\"lines\": [{\"text\": \"\", \"box\": [x1, y1, x2, y2]}]}\\n{coord_hint} '\n", " \"If you cannot localize a line, omit its box.\",\n", "}\n", "def _patched_resolved(spec):\n", " p = _V2_PROMPTS.get(spec.category)\n", " if p is None:\n", " return spec.system_prompt\n", " return p.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", "_R.resolved_system_prompt = _patched_resolved\n", "print(\"prompts patched (anti-fence + explicit schema). constrained backend will be:\",\n", " \"xgrammar\" if XGRAMMAR else \"json_mode FALLBACK (xgrammar missing)\")\n", "\n", "# ---- 5. re-run the probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40])); continue\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None) # confirms constrained is real\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, res.backend + \"/\" + mode, \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ PROBE v2 (constrained ON + anti-fence prompts) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (json_mode only) | constrained-valid (xgrammar forces schema) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " cn = [r for r in rows if r[0] == mkey and \"constrained\" in r[2]]\n", " if not jm: continue\n", " v_jm = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " v_cn = sum(1 for r in cn if r[3] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v_jm else \"needs-look\")\n", " print(f\" {mkey:30s} json_mode valid {v_jm}/{len(jm)} robust {rob}/{len(jm)} -> {bucket} \"\n", " f\"| constrained valid {v_cn}/{len(cn)}\")\n", "print(\"\\nPaste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 5 — BIGGER BRAINS: native-JSON probe across the larger ladder rungs (json_mode).\n", "# Run after CELL 4 (deps already installed; package has the anti-fence v2 prompts).\n", "# json_mode only = the native no-finetune signal. Clears each model's HF cache after use\n", "# so ~220GB of weights don't fill the 230GB SSD (set CLEAR_CACHE_AFTER=False to keep them).\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit these ----\n", "MODELS = [\"qwen3.5-9b\", \"qwen3.5-27b\", \"qwen3.5-35b-a3b\", \"qwen3vl-8b\", \"qwen3vl-32b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "CLEAR_CACHE_AFTER = True # delete each model's weights from disk after probing it\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", " print(\" cleared cache:\", p)\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, ~{spec.est_vram_gb('bf16'):.0f}GB, MoE={spec.is_moe}) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(\" LOAD FAILED:\", type(e).__name__, str(e)[:160])\n", " rows.append((mkey, \"-\", \"json_mode\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " try:\n", " res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=\"json_mode\", image_id=\"probe\",\n", " image_size=img.size, n_output_tokens=res.n_output_tokens,\n", " gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, \"json_mode\", \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, \"json_mode\", \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", "\n", "hdr = [\"model\", \"category\", \"mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ BIGGER-BRAINS PROBE (json_mode native) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (clean valid JSON on its own, across all 3 categories) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[3] in (\"Y\", \"n\")]\n", " if not jm:\n", " print(f\" {mkey:18s} (no rows / load failed)\"); continue\n", " v = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v else \"needs-look\")\n", " print(f\" {mkey:18s} valid {v}/{len(jm)} robust {rob}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole BIGGER-BRAINS block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d4f06b6f8bab4bc6bdeb757da5e62428", "b2bc952da5f245ecadfc11c7bb6a9ed5", "70b55cdaf6b345f496c79c29a25bff43", "68fc7656250344edb41aa80f030fe777", "5581691503204cecb4d59703e503faae", "6ebd8c96056f4b0fa190b93cf52be8e8", "011f0a35c7e5477c8e32f4cb309e572f", 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tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260627T214511_620420Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00\n", "\n", "{\"label\": \"color\", \"confidence\": 1.0}\n", "\n", "\n", "schema_valid: True | needed_repair: True | json_robust: False | out_tokens: 52 | gen_seconds: 0.72\n", "REAL VLMRunner PATH WORKS.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\n", "\n", "$ pip -q install flash-linear-attention || echo 'fla install failed — ok'\n", "\n", "$ pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\n", "\n", "##### loading qwen3.5-0.8b-json-captioner (AbstractPhil/Qwen3.5-0.8B-json-captioner, est 1.7GB bf16) #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate\n", " qwen3.5-0.8b valid 1/3 robust 0/3 -> finetune_candidate\n", " qwen3.5-2b valid 3/3 robust 3/3 -> native_capable\n", " qwen3.5-4b valid 3/3 robust 2/3 -> finetune_candidate\n", "\n", "Paste this whole PROBE block back.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed'\n", "\n", "=== installing xgrammar (FULL output so we can see any error) ===\n", "\n", "xgrammar import OK — version ?\n", "prompts patched (anti-fence + explicit schema). constrained backend will be: xgrammar\n", "\n", "##### loading qwen3.5-0.8b-json-captioner #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate | constrained valid 2/3\n", " qwen3.5-0.8b json_mode valid 1/3 robust 0/3 -> finetune_candidate | constrained valid 1/3\n", " qwen3.5-2b json_mode valid 3/3 robust 3/3 -> native_capable | constrained valid 3/3\n", " qwen3.5-4b json_mode valid 3/3 robust 2/3 -> finetune_candidate | constrained valid 3/3\n", "\n", "Paste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\n", "\n", "##### loading qwen3.5-9b (Qwen/Qwen3.5-9B, ~18GB, MoE=False) #####\n", "[VLMRunner] loading Qwen/Qwen3.5-9B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00 native_capable\n", " qwen3.5-27b valid 3/3 robust 2/3 -> finetune_candidate\n", " qwen3.5-35b-a3b valid 3/3 robust 1/3 -> finetune_candidate\n", " qwen3vl-8b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-32b valid 3/3 robust 3/3 -> native_capable\n", "\n", "Paste this whole BIGGER-BRAINS block back.\n" ] } ] }, { "cell_type": "code", "source": [ "# ==========================================================================================\n", "# qwen-vlmbench — Colab cells for the RTX 6000 Pro (96GB)\n", "#\n", "# HOW TO USE\n", "# 1. Drag qwen_vlmbench_colab.zip into the Colab file browser (it lands in /content/).\n", "# 2. Paste CELL 1 into a Colab cell and run it. It unzips, installs, and VERIFIES the\n", "# qwen_test_runner + vision harness with NO model download (fast, ~1-2 min).\n", "# 3. (Optional) Paste CELL 2 into a SEPARATE cell to load a real Qwen VLM and run the\n", "# full pipeline on it. This one downloads weights + transformers@main (heavier).\n", "#\n", "# These are independent cells — copy each block into its own Colab cell.\n", "# ==========================================================================================\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 1 — ESTABLISH THE SYSTEM (install + verify, no model download)\n", "# ==========================================================================================\n", "import glob, os, subprocess, zipfile\n", "\n", "def sh(cmd):\n", " print(\"\\n$ \" + cmd)\n", " subprocess.run(cmd, shell=True)\n", "\n", "# 1) locate + unzip the uploaded package\n", "zips = glob.glob(\"/content/qwen_vlmbench_colab*.zip\") + glob.glob(\"qwen_vlmbench_colab*.zip\")\n", "assert zips, \"Upload qwen_vlmbench_colab.zip to /content first (drag it into the file browser).\"\n", "ZIP = sorted(zips)[-1]\n", "with zipfile.ZipFile(ZIP) as z:\n", " z.extractall(\"/content\")\n", "PKG = \"/content/qwen-test-runner\"\n", "assert os.path.isdir(PKG), f\"expected {PKG} after unzip; got {os.listdir('/content')}\"\n", "print(\"unzipped:\", ZIP, \"->\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 3 — NATIVE-JSON CAPABILITY PROBE across the small-model ladder\n", "# Answers the investigation's headline question: which base models emit clean per-task\n", "# JSON *natively* (native_capable) vs need repair (finetune_candidate)? Also validates the\n", "# throughput fix (causal-conv1d fast path). Uses synthetic probe images, so it needs NO\n", "# ground-truth download — it measures JSON shape + robustness + speed, not accuracy.\n", "# Output is a compact table to paste back.\n", "#\n", "# Edit MODELS to add/remove rungs. Small first: 0.8B captioner vs 0.8B base shows what the\n", "# LoRA did to native JSON shape; 2B/4B/9B show whether bigger base models are clean natively.\n", "# ==========================================================================================\n", "import subprocess, time\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps (idempotent) + the fast-path libs that were MISSING in the CELL-2 run (8 tok/s).\n", "# On Colab Linux these build fine (unlike the Windows dev box).\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\")\n", "sh(\"pip -q install flash-linear-attention || echo 'fla install failed — ok'\")\n", "sh(\"pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\")\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit this list to choose the ladder rungs to probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"] # constrained needs xgrammar (falls back if missing)\n", "\n", "# ---- synthetic probe images (offline, deterministic) ----\n", "def _img_classification():\n", " return Image.new(\"RGB\", (256, 256), (40, 90, 160)) # a solid blue image\n", "\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)) # red box\n", " d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)) # blue box\n", " return im\n", "\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.text((40, 45), \"STOP\", fill=(0, 0, 0))\n", " return im\n", "\n", "PROBE_IMAGES = {\n", " \"image_classification\": _img_classification(),\n", " \"bbox_grounding\": _img_detection(),\n", " \"ocr_text\": _img_ocr(),\n", "}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, est {spec.est_vram_gb('bf16'):.1f}GB bf16) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(f\" LOAD FAILED: {type(e).__name__}: {str(e)[:200]}\")\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat)\n", " img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " snippet = res.raw_text.replace(\"\\n\", \" \")[:48]\n", " rows.append((mkey, cat, res.backend + \"/\" + mode,\n", " \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\",\n", " \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", snippet))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "# ---- compact, paste-friendly table ----\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ NATIVE-JSON CAPABILITY PROBE ================\\n\")\n", "print(line(hdr))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(line(r))\n", "\n", "# per-model native verdict (json_mode only = no grammar crutch)\n", "print(\"\\n---- native bucket (json_mode: emits clean valid JSON on its own?) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " if not jm:\n", " continue\n", " valid = sum(1 for r in jm if r[3] == \"Y\")\n", " robust = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if robust == len(jm) else (\"finetune_candidate\" if valid else \"insufficient/needs-look\")\n", " print(f\" {mkey:30s} valid {valid}/{len(jm)} robust {robust}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole PROBE block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 4 — FIX: turn ON constrained decoding (xgrammar) + stronger anti-fence prompts,\n", "# then re-run the probe.\n", "#\n", "# IMPORTANT: run this on a FRESH kernel. First do Runtime -> Restart runtime (this keeps\n", "# the downloaded models on disk, so no re-download). xgrammar must be installed BEFORE\n", "# qwen_test_runner is imported, or its constrained backend stays off.\n", "#\n", "# What changes vs CELL 3:\n", "# * installs xgrammar FIRST and shows any error (CELL 3 swallowed it -> constrained was a no-op)\n", "# * patches the 3 pilot prompts to forbid ``` fences and show the exact schema + the\n", "# 'box' key (so json_mode \"native\" numbers improve; mirrors the v2 zip)\n", "# * silences the noisy [transformers] processor-kwargs warning\n", "# With xgrammar live, the 'constrained' rows should show backend=xgrammar and flip to valid+robust.\n", "# ==========================================================================================\n", "import subprocess\n", "\n", "# ---- 1. install deps + xgrammar BEFORE importing qwen_test_runner ----\n", "def sh(c):\n", " print(\"\\n$ \" + c); subprocess.run(c, shell=True)\n", "\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed'\")\n", "print(\"\\n=== installing xgrammar (FULL output so we can see any error) ===\")\n", "subprocess.run(\"pip install xgrammar\", shell=True)\n", "try:\n", " import xgrammar as _xgr\n", " print(\"\\nxgrammar import OK — version\", getattr(_xgr, \"__version__\", \"?\"))\n", " XGRAMMAR = True\n", "except Exception as e:\n", " print(\"\\nxgrammar import FAILED:\", type(e).__name__, \"::\", str(e)[:400])\n", " XGRAMMAR = False\n", "\n", "# ---- 2. silence the benign [transformers] processor-kwargs spam ----\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers\n", " transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "# ---- 3. import the harness (AFTER xgrammar so its backend is detected) ----\n", "import time\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "# ---- 4. patch in the stronger anti-fence prompts (mirrors the v2 zip) ----\n", "_V2_PROMPTS = {\n", " \"image_classification\":\n", " \"You are an image classifier. Identify the single most prominent object or scene \"\n", " \"category in the image. Output ONLY a raw JSON object and NOTHING else — no prose, \"\n", " \"no explanation, and NO markdown code fences (do not wrap it in ```). It must match \"\n", " 'this shape exactly:\\n{\"label\": \"\", \"confidence\": , '\n", " '\"top5\": [{\"label\": \"\", \"score\": }]}',\n", " \"bbox_grounding\":\n", " \"You are an object detector. Find every distinct object in the image. Output ONLY a \"\n", " \"raw JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it \"\n", " 'in ```). It must match this shape exactly:\\n{\"detections\": [{\"label\": \"\", '\n", " '\"box\": [x1, y1, x2, y2], \"score\": }], \"count\": }\\n{coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with exactly four numbers [x1, y1, x2, y2].',\n", " \"ocr_text\":\n", " \"You are an OCR engine. Transcribe all readable text in the image. Output ONLY a raw \"\n", " \"JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it in \"\n", " '```). It must match this shape exactly:\\n{\"full_text\": \"\", '\n", " '\"lines\": [{\"text\": \"\", \"box\": [x1, y1, x2, y2]}]}\\n{coord_hint} '\n", " \"If you cannot localize a line, omit its box.\",\n", "}\n", "def _patched_resolved(spec):\n", " p = _V2_PROMPTS.get(spec.category)\n", " if p is None:\n", " return spec.system_prompt\n", " return p.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", "_R.resolved_system_prompt = _patched_resolved\n", "print(\"prompts patched (anti-fence + explicit schema). constrained backend will be:\",\n", " \"xgrammar\" if XGRAMMAR else \"json_mode FALLBACK (xgrammar missing)\")\n", "\n", "# ---- 5. re-run the probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40])); continue\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None) # confirms constrained is real\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, res.backend + \"/\" + mode, \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ PROBE v2 (constrained ON + anti-fence prompts) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (json_mode only) | constrained-valid (xgrammar forces schema) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " cn = [r for r in rows if r[0] == mkey and \"constrained\" in r[2]]\n", " if not jm: continue\n", " v_jm = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " v_cn = sum(1 for r in cn if r[3] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v_jm else \"needs-look\")\n", " print(f\" {mkey:30s} json_mode valid {v_jm}/{len(jm)} robust {rob}/{len(jm)} -> {bucket} \"\n", " f\"| constrained valid {v_cn}/{len(cn)}\")\n", "print(\"\\nPaste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 5 — BIGGER BRAINS: native-JSON probe across the larger ladder rungs (json_mode).\n", "# Run after CELL 4 (deps already installed; package has the anti-fence v2 prompts).\n", "# json_mode only = the native no-finetune signal. Clears each model's HF cache after use\n", "# so ~220GB of weights don't fill the 230GB SSD (set CLEAR_CACHE_AFTER=False to keep them).\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit these ----\n", "MODELS = [\"qwen3.5-9b\", \"qwen3.5-27b\", \"qwen3.5-35b-a3b\", \"qwen3vl-8b\", \"qwen3vl-32b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "CLEAR_CACHE_AFTER = True # delete each model's weights from disk after probing it\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", " print(\" cleared cache:\", p)\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, ~{spec.est_vram_gb('bf16'):.0f}GB, MoE={spec.is_moe}) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(\" LOAD FAILED:\", type(e).__name__, str(e)[:160])\n", " rows.append((mkey, \"-\", \"json_mode\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " try:\n", " res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=\"json_mode\", image_id=\"probe\",\n", " image_size=img.size, n_output_tokens=res.n_output_tokens,\n", " gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, \"json_mode\", \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, \"json_mode\", \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", "\n", "hdr = [\"model\", \"category\", \"mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ BIGGER-BRAINS PROBE (json_mode native) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (clean valid JSON on its own, across all 3 categories) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[3] in (\"Y\", \"n\")]\n", " if not jm:\n", " print(f\" {mkey:18s} (no rows / load failed)\"); continue\n", " v = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v else \"needs-look\")\n", " print(f\" {mkey:18s} valid {v}/{len(jm)} robust {rob}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole BIGGER-BRAINS block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 6 — REAL GROUND TRUTH: COCO detection + coordinate-scale CALIBRATION.\n", "# Before trusting any IoU, we must know what coordinate scale the model emits. This cell\n", "# runs qwen3-vl-8b (the native grounding champion) on real COCO val images and scores\n", "# detection under ALL 3 coord spaces x BOTH box formats — the combo with the best F1\n", "# reveals the model's true convention. It also prints raw model output + GT so we can see.\n", "#\n", "# Run on a fresh-ish kernel OR after CELL 4/5 (needs: datasets, the harness, a GPU).\n", "# ==========================================================================================\n", "import warnings, itertools\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from datasets import load_dataset\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task, model_for\n", "from qwen_test_runner.vision.metrics import score_detection\n", "from qwen_test_runner.vision.coords import CoordSpace, XYWH, XYXY\n", "from qwen_test_runner.evaluator import parse_against\n", "\n", "N = 10\n", "MODEL = \"qwen3vl-8b\"\n", "\n", "# ---- load N COCO val samples (streaming) and SHOW the schema so we can see GT format ----\n", "def _load_coco(n):\n", " last_err = None\n", " for repo, split in [(\"detection-datasets/coco\", \"val\"), (\"detection-datasets/coco\", \"validation\"),\n", " (\"rafaelpadilla/coco2017\", \"val\")]:\n", " try:\n", " ds = load_dataset(repo, split=split, streaming=True)\n", " rows = list(itertools.islice(ds, n))\n", " print(f\"loaded {len(rows)} from {repo}:{split}\")\n", " return rows, ds\n", " except Exception as e:\n", " last_err = e\n", " print(f\" {repo}:{split} failed -> {type(e).__name__}: {str(e)[:120]}\")\n", " raise last_err\n", "\n", "rows, ds = _load_coco(N)\n", "print(\"\\n=== dataset features ===\")\n", "try:\n", " print(ds.features)\n", "except Exception as e:\n", " print(\"features unavailable:\", e)\n", "print(\"\\n=== first sample objects (raw) ===\")\n", "print({k: (v if k != \"image\" else f\"\") for k, v in rows[0].items()})\n", "\n", "# ---- category id -> name map (best effort) ----\n", "cat_names = None\n", "try:\n", " obj_feat = ds.features[\"objects\"]\n", " inner = obj_feat.feature if hasattr(obj_feat, \"feature\") else obj_feat\n", " cat_feat = inner[\"category\"] if isinstance(inner, dict) else None\n", " cat_names = getattr(getattr(cat_feat, \"feature\", cat_feat), \"names\", None)\n", "except Exception as e:\n", " print(\"cat-name lookup failed:\", e)\n", "def _cat(cid):\n", " if cat_names and isinstance(cid, int) and 0 <= cid < len(cat_names):\n", " return cat_names[cid]\n", " return str(cid)\n", "\n", "# ---- build GT samples from whatever schema we got ----\n", "def _gt_from(sample):\n", " img = sample[\"image\"]\n", " objs = sample.get(\"objects\", {})\n", " bboxes = objs.get(\"bbox\") or objs.get(\"bboxes\") or []\n", " cats = objs.get(\"category\") or objs.get(\"categories\") or objs.get(\"label\") or []\n", " boxes = [{\"label\": str(_cat(c)).lower().replace(\"_\", \" \"), \"bbox\": list(map(float, b))}\n", " for b, c in zip(bboxes, cats)]\n", " return img, boxes, img.size\n", "\n", "samples = [_gt_from(r) for r in rows]\n", "print(f\"\\nfirst GT boxes: {samples[0][1][:3]} | image size {samples[0][2]}\")\n", "\n", "# ---- run the model ----\n", "spec = get_model(MODEL)\n", "task = get_task(\"bbox_grounding\")\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", "preds = []\n", "for i, (img, boxes, size) in enumerate(samples):\n", " res = runner.generate(task, img, \"json_mode\", image_id=f\"coco{i}\", image_size=size)\n", " preds.append(res.raw_text)\n", " if i < 2:\n", " print(f\"\\n--- sample {i} (size {size}) RAW MODEL OUTPUT ---\\n{res.raw_text[:400]}\")\n", " print(f\"--- sample {i} GT boxes (pixel) ---\\n{boxes[:4]}\")\n", "runner.close()\n", "\n", "# ---- CALIBRATE: score detection under every coord_space x box-fmt; best F1 = the convention ----\n", "Model = model_for(task)\n", "print(\"\\n================ COORDINATE CALIBRATION (mean detection F1) ================\")\n", "print(f\"{'coord_space':14s} {'gt_fmt':6s} meanF1 (over {N} images)\")\n", "best = (None, None, -1.0)\n", "for space in [CoordSpace.NORM_0_1000, CoordSpace.NORM_0_1, CoordSpace.PIXEL_ABS]:\n", " for fmt in [XYWH, XYXY]:\n", " f1s = []\n", " for (img, boxes, size), raw in zip(samples, preds):\n", " pr = parse_against(raw, Model)\n", " if not pr.schema_valid:\n", " f1s.append(0.0); continue\n", " gt = {\"boxes\": [{\"label\": b[\"label\"], \"bbox\": b[\"bbox\"], \"fmt\": fmt} for b in boxes]}\n", " ctx = {\"size\": size, \"coord_space\": space}\n", " try:\n", " primary, _ = score_detection(pr.parsed.model_dump(), gt, ctx)\n", " f1s.append(primary or 0.0)\n", " except Exception:\n", " f1s.append(0.0)\n", " mean = sum(f1s) / len(f1s) if f1s else 0.0\n", " print(f\"{space.value:14s} {fmt:6s} {mean:.3f}\")\n", " if mean > best[2]:\n", " best = (space.value, fmt, mean)\n", "print(f\"\\nBEST: coord_space={best[0]} gt_fmt={best[1]} meanF1={best[2]:.3f}\")\n", "print(\"(Low across the board can mean labels mismatch COCO names, not coords — check the RAW vs GT above.)\")\n", "print(\"\\nPaste this whole CELL-6 block back (especially the RAW outputs, GT boxes, and the calibration table).\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "61945ed99b3d4952ad0179dbecc15c17", "d5a68c22a374454d9afa54eb9a2abd13", "2d7214bd242944d9b72d19971d90e075", "587880ee15984ca98f345f2cac86c9e1", "e0783dbd1dca4e33a960e1f5709d400b", "e42f673514664b2ab71051c908adcaa7", "60a15b55b3e0430889744ef468a01d86", "6f70a722111245cbbdf4eac417b606f2", "9fb2e317c3be4a02ab56ba966e3dea27", "1da4e1e3bb824b6f87a847ab54635d9e", "ca280df0283a4209ba1ea7d49a93087b", "6cf817c94e8a4913a56c0eddaf50d85b", "405f741392834c07b581e52a760f29b7", "1e91cb609d1c41e59f1046d95cd614bd", "183e362d1b744937b4c7f5c7b42253c9", "f676bba2af9a44b7ba41ab5faaf2db28", "a23b1cadeb354e64b7a20d2c3fce5629", "2733d072a9a7471989dc9a3d30b17b16", "90cec4df1b9a4bbfb1712543bcb50276", "7df96da803374ed180e6df653b1d4c6e", "9f3635fc99434c8f9d4233c331c758ae", "954a77174227441c8f06d8257f7cddf8", "561e04e2ad184fa6a34627e00bd0f901", "eaf13d2ddf7148e1bff452b3f18a951a", "066f6d185c3c4eb2b6db70335f3c01f1", "fd46fdc3c1754d1b8dd6c78ef219c389", "48f10f16fa3c4aa882647e6a601b966e", "4068a949788a493eb41d0a1d1f9b3929", "f0744f7d4d30410c99fa2ab524ed9a6e", "45d429d28e894723b6da0d3107706222", "f5144bd8c18147cfb89b26b01ad2c593", "73f0b42968c1492b8a0d2d4a59c7611b", "532c929f3f464c258c7e88fa6fc663a9", "5dbc2777c90f4913bfad90ae2a5297e3", "4eb40fce191c4dbb8175bf49a6c216c6", "c57d2a3e58594bc58b60040d957e5c0a", "2d77146f5233404ab77e3181bf71e465", "36f08be6a45d44d5a3ed46e727556754", "65eba36d980949c8bf722ad7b9b86f38", "1d85b4e2de33474abfdbbb2eb5c79c8a", "d6e080a5e1df4a60bac0445fc1e63891", "214ec23955ab4c0c9b5194e98ae02d3d", "730f7862a5cb43608698b18f8228b591", "c3813772f0544c7cb46654bf4b867eef", 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"161d108b31a74356990b985c3f73fa2b", "dc12d4d0f0a547f6a80bad7ed9612b9f", "0dcd31ce43dc4cfcb9d488ecddb1d59e", "7bfece060f0f4132ae166559fed86ad6", "6130c170ea9f433887382e76d0e0c804", "32ef0ed1a5ff4b078339474e12c1e222", "70090f4637ab43619b9f744ad78e0576", "dc97ad3404854139a7054fad68c4a492", "d9f8e91d2a184e2e8f17fdf47fe2fcc7", "093ca71e77fb4694be912f354bdd006b", "989d5f1f5222494db9813206550dd838", "4a8f23e8e64545dfa13060744f61eb0e", "43fc8371fcf546c6bc72643e6aaea134", "fa4035b480b54f70ba7b1d36c0fd0163", "06bc7cdb3d5c4836afc21036f3fc078f", "af59ccfde4a64264b8a9d9eb88b371ff", "09db39b72a9f459384e6326e64e22d75" ] }, "id": "uIqUH-uKQ1id", "outputId": "41f03195-8ee3-4eb4-dfe3-6b1717a7677d" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: /content/qwen_vlmbench_colab_v2.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260627T224653_968020Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00\n", "\n", "{\"label\": \"color\", \"confidence\": 1.0}\n", "\n", "\n", "schema_valid: True | needed_repair: True | json_robust: False | out_tokens: 52 | gen_seconds: 0.72\n", "REAL VLMRunner PATH WORKS.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\n", "\n", "$ pip -q install flash-linear-attention || echo 'fla install failed — ok'\n", "\n", "$ pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\n", "\n", "##### loading qwen3.5-0.8b-json-captioner (AbstractPhil/Qwen3.5-0.8B-json-captioner, est 1.7GB bf16) #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate\n", " qwen3.5-0.8b valid 1/3 robust 0/3 -> finetune_candidate\n", " qwen3.5-2b valid 3/3 robust 3/3 -> native_capable\n", " qwen3.5-4b valid 3/3 robust 2/3 -> finetune_candidate\n", "\n", "Paste this whole PROBE block back.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed'\n", "\n", "=== installing xgrammar (FULL output so we can see any error) ===\n", "\n", "xgrammar import OK — version ?\n", "prompts patched (anti-fence + explicit schema). constrained backend will be: xgrammar\n", "\n", "##### loading qwen3.5-0.8b-json-captioner #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate | constrained valid 2/3\n", " qwen3.5-0.8b json_mode valid 1/3 robust 0/3 -> finetune_candidate | constrained valid 1/3\n", " qwen3.5-2b json_mode valid 3/3 robust 3/3 -> native_capable | constrained valid 3/3\n", " qwen3.5-4b json_mode valid 3/3 robust 2/3 -> finetune_candidate | constrained valid 3/3\n", "\n", "Paste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\n", "\n", "##### loading qwen3.5-9b (Qwen/Qwen3.5-9B, ~18GB, MoE=False) #####\n", "[VLMRunner] loading Qwen/Qwen3.5-9B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00 native_capable\n", " qwen3.5-27b valid 3/3 robust 2/3 -> finetune_candidate\n", " qwen3.5-35b-a3b valid 3/3 robust 1/3 -> finetune_candidate\n", " qwen3vl-8b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-32b valid 3/3 robust 3/3 -> native_capable\n", "\n", "Paste this whole BIGGER-BRAINS block back.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/58.0 [00:00', 'width': 640, 'height': 426, 'objects': {'bbox_id': [26547, 34646, 35802, 103487, 104368, 105328, 110334, 230831, 233201, 1640282, 1647285, 1648594, 1654394, 1666628, 1667817, 1669970, 1941808, 2146194, 2146548, 2204286], 'category': [58, 62, 62, 56, 56, 56, 56, 0, 0, 68, 72, 73, 73, 74, 75, 75, 56, 75, 75, 60], 'bbox': [[236.98, 142.51, 261.68, 212.01], [7.03, 167.76, 156.35, 262.63], [557.21, 209.19, 638.5600000000001, 287.92], [358.98, 218.05, 414.98, 320.88], [290.69, 218.0, 352.52, 316.48], [413.2, 223.01, 443.37, 304.37], [317.4, 219.24, 338.97999999999996, 230.83], [412.8, 157.61, 465.85, 295.62], [384.43, 172.21, 399.55, 207.95000000000002], [512.22, 205.75, 526.96, 221.72], [493.1, 174.34, 513.39, 282.65], [604.77, 305.89, 619.11, 351.59999999999997], [613.24, 308.24, 626.12, 354.68], [447.77, 121.12, 461.74, 143.0], [549.06, 309.43, 585.7399999999999, 399.1], [350.76, 208.84, 362.13, 231.39000000000001], [412.25, 219.02, 421.88, 231.54000000000002], [241.24, 194.99, 255.46, 212.62], [336.79, 199.5, 346.52000000000004, 216.23], [321.21, 231.22, 446.77, 320.15]], 'area': [531.8071000000001, 13244.657700000002, 5833.117949999999, 2245.34355, 1833.7840000000017, 1289.3734500000014, 210.14820000000023, 2913.1103999999987, 435.1449499999997, 217.71919999999997, 2089.9747999999986, 338.60884999999973, 322.5935999999982, 225.6642000000005, 2171.6188500000007, 178.18510000000012, 90.98724999999988, 189.56010000000012, 120.23200000000004, 2362.4897499999984]}}\n", "\n", "first GT boxes: [{'label': 'potted plant', 'bbox': [236.98, 142.51, 261.68, 212.01]}, {'label': 'tv', 'bbox': [7.03, 167.76, 156.35, 262.63]}, {'label': 'tv', 'bbox': [557.21, 209.19, 638.5600000000001, 287.92]}] | image size (640, 426)\n", "[VLMRunner] loading Qwen/Qwen3-VL-8B-Instruct (image_text_to_text, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "# %% =======================================================================================\n", "# CELL 7 — REAL DETECTION ACCURACY (v3): native models on real COCO, with the upgraded\n", "# scorer (tolerant labels + class-agnostic localization).\n", "#\n", "# REQUIRES THE v3 ZIP. Do: Runtime -> Restart runtime, drag qwen_vlmbench_colab_v3.zip\n", "# into /content, re-run CELL 1 (it re-extracts + reinstalls), THEN run this cell.\n", "# (Restart matters so the new package + xgrammar-before-import both take effect.)\n", "#\n", "# Reports per model: labeled F1 (tv==television etc.), class-agnostic LOCALIZATION F1\n", "# (can it box objects at all), recall, count error, tok/s. Clears HF cache between models.\n", "# ==========================================================================================\n", "import os, shutil, warnings, subprocess\n", "warnings.filterwarnings(\"ignore\")\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "# xgrammar BEFORE importing the harness so v3's lazy detection turns constrained ON\n", "subprocess.run(\"pip -q install xgrammar\", shell=True)\n", "try:\n", " import xgrammar; print(\"xgrammar:\", getattr(xgrammar, \"__version__\", \"?\"))\n", "except Exception as e:\n", " print(\"xgrammar not available:\", e)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from qwen_test_runner.vision.datasets import load_coco_detection\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"] # native champions; edit freely\n", "N = 20\n", "MODES = [\"json_mode\"] # add \"constrained\" to test the grammar path (v3 lazy-xgrammar)\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "print(f\"\\nloading {N} COCO val images …\")\n", "samples = load_coco_detection(n=N, split=\"val\")\n", "print(f\"got {len(samples)} samples; first GT: {samples[0].gt['boxes'][:2]} size={samples[0].size}\")\n", "\n", "spec = get_task(\"bbox_grounding\")\n", "summary = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} ({ms.repo_id}) #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None)\n", " try:\n", " for mode in MODES:\n", " results = []\n", " for s in samples:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " results.append((mr, res))\n", " if s is samples[0]:\n", " print(f\" [{mode}] sample0 raw: {res.raw_text[:160]}\")\n", " mrs = [r[0] for r in results]\n", " run = score_vision_run(mrs, model=mkey, category=\"bbox_grounding\", mode=mode)\n", " loc = sum(r.metrics.get(\"localization_f1\", 0.0) for r in mrs if r.schema_valid)\n", " rec = sum(r.metrics.get(\"recall\", 0.0) for r in mrs if r.schema_valid)\n", " cerr = sum(r.metrics.get(\"count_abs_err\", 0.0) for r in mrs if r.schema_valid)\n", " nv = sum(1 for r in mrs if r.schema_valid) or 1\n", " summary.append({\n", " \"model\": mkey, \"mode\": mode, \"backend\": results[0][1].backend,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"labeledF1\": run.primary_score_mean or 0.0,\n", " \"localF1\": loc / nv, \"recall\": rec / nv, \"count_err\": cerr / nv,\n", " \"tok_s\": run.tokens_per_sec,\n", " })\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "print(\"\\n================ REAL COCO DETECTION (N=%d) ================\\n\" % N)\n", "hdr = [\"model\", \"mode\", \"backend\", \"valid\", \"robust\", \"labeledF1\", \"localF1\", \"recall\", \"count_err\", \"tok/s\"]\n", "def fmt(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(fmt(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + summary)) for h in hdr]\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in summary:\n", " print(\" \".join(fmt(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nlabeledF1 = boxes matched WITH a tolerant label (tv==television); \"\n", " \"localF1 = boxes matched ignoring labels (pure localization).\")\n", "print(\"Paste this whole CELL-7 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "7d56d089467842ec9cfdd4408ad1e3dd", "3a7c88336fb4419193deaa5a29d3ca02", "470f39598d9e494aa262dae6365b0ecd", "4244a320a2194e96b570c3f7b076758b", "0a8a54b8973344a4a9f148002238aecc", "46400e08fe904eada87a21a33ab98dc2", "0cf4eb6de66346569de72e4a9ec61084", "1ecb4456f10d4144a4d1302d736b50da", "4ea8c26675e641639cb28c20f58b2062", "a5ee76f2970e42af94cf554ddfb7524c", "c312a1f2c6fb4adeb20bbb6a2e4ff574", "111e433521924f8e9ddebdc583d016ef", "b5d5ddecb5f542b3a24b12e90a585130", "d77afe0bb82f4296b8882e1e706860c7", 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"dffb913520eb40da969183456236774f", "e0ce4e5d64554b0cb27198cdd8888684", "ec37c836f5504bec89adc3befee7b63f", "4d95808c03824083884debadfba6ed02", "c8350654c1224f1fa67e2d4d9f19223a", "26147a648e1f474f89a90cfcbf923372", "b48fbbc8f83a4a0196d9ffcbc9cb0ae9", "bd685837258f4e218c0b186f6a8e2c3a", "3535796e1bf7414297fba04e003d6f2e", "676f279fb3a346dcb25b96718f5f2d82", "6bef5a4c480c4ea0847bb1059cca48a3", "acdfecbfa13c4664a22fd3ae94672fd8", "dcbf5645b2b746f99641410577313fd9", "7268e62c8a824e3684839f04f3857490", "09563549b0d846f4bc78a5fa442681ef", "5d2ae5ae011842238288c85e646734ab", "8c9e5b3acfc1452e88adc4133cc667ef", "e877994feb5a4a5a9d04910a1f40be32", "26828e8d350a4cdda9336ddf41322375", "8dbfd1fa8e3e42da97a6a9f71ad85ead", "0c4ca337290a47cabecf6e4172f8f254", "7ed5615954f24efda85e410cfd989c9d", "abe8a8f41032409184c3efc2fb35a287", "ba5b4602aeb54eb689e1fd2aaab74a81", "ec4e8cfc2d2a4f2fa0044eb8a469e45c", "a442b4d64f5343569eda6e72e5d67f7b", "ce96d5aa7f014c8d920c04fa748864fe", "b455f69eab124497b73cfb0249f16149", "011f4ff2b16a4b7cb633358adea326ed", "4d65b121c14d44fa9b6b4863e02284fa", "6a5f39d21942427fae1f80d835bd409a", "8c191354bc33496295528900278808ec", "0647c4f28de04f0b99c6eb57df368d3f", "c64b7c7317ae4d83a7c420738e8717fd", "b7a22cfd4f3a4d71b4eb9c75b54a55b4", "5d61b5d678204dd39b1713f847f7d72c", "a25a3aa0e78e4462b5f99ce2a2d81080", "00db27da94964d74acc3f9343c5d4f28", "6345753b0fd249aaa8e16a251f47fff1", "9bf7cdc1766a4ef78296458b146aee3f", "29876ddfb0ca47f2b9c2ac391b81bd95", "d02d2092a51944f8b332cf2cbcad564f", "7743041f51a54963af0fdf117c6f9731", "83f8bfe0111240bbbd01d1b27b601fe9", "94e287901a2e4e9c84ba86eb831d6d4b", "50157fa256f1416b94fc6abe81b12aa5", "00cd1c366b29411ba8ec352db89a21bb", "a84e8e82afb646ef965b20d3ac35ff8d", "e11952ea05ff4289b1bcb838b25dfdb8", "f3153bfb2283487ea13972f3a60cba4b", "3148e70d555b49b79f66640bb972d42d", "e6f4308652634e3ebe9fa828f566d110", "e91ae77e79444bf5839fe5a409064459", "c73280cae2794b30a84abc995216a1c8", "8e339712c2b142a4b55dc88d854d3fcf", "392d5f867606462a88ba50c9bb36ca62", "948fd84d7832459688621787b79ae024", "0811c140180944dc833a23c32ed348d1", "6deb34fd5a6a4b52aba6856890ba6203", "d61f09d3011c4875aff5d1e0c6ae11a6", "751d1fdeee7641618c9d03c8fc6c225f", "e36b0b836749458191c3ad0239b035a1", "9c4486f5b4ec42ed8fd18b2487d0bb0a", "5ef5eb7087c74a209715aa434637cfcb", "02ee0e2734ef4c98b0125214baf23ecf", "205ec3d55dd940348e912c8f7f87e4a3", "97992aa541a344b8bf7ce941fbdad739", "f894853396c94b7590d986227bd3dba7", "2f52f63721db4ccabfaa3771e9262faa", "a8a78aee880747c89045b6a2015ffaa3", "db568c39733941c084a6e504e1302990", "ae8621fc7ad946fbae7fd058b9731d16", "7015d97078df41dd948bcadce5191d28", "b5f0fedef56f44edbff6136dc285466a", "aec633944cc34140acf6cb64107988f9", "236b4c210c1f41259202e28010a04f80", "7a8881eece7f430dbff268d8bccf3c88", "2a69a6fd176b411ea9306558d32f0d2d", "6db0d8926f574b56847834ea8a1f03eb", "4e34ef72b7c54d6abeb880e9d9b36dfb", "1ad1b2a1bbb0494e8e5d0560a74eaf41", "a6b0a57f5cc4419d8cd2c285d2b6a850", "941efd350e0c49fc9085daf4ed928dcd", "7b026725deb04e3784d5f9aa45289c7d", "394160a5eb8a4f7384734edb974a7f8e", "261aea88cf7e461bba3b59a361dcdbcc", "a6330f3dd8ac4cffbfcd26a56fc319e9", "da64de9fb2b647cfa870d31ec042d79b", "3c5af0fcbd7c44c9be4d447ab5bcab2a", "04bc02373eb646f78c3d76c3c229370e", "5bb53c41bcb64ecbb425fe652ef972d4", "6e01e982a8604a9b9c6e25df5595db3f", "78729b58468f4be0a7192f6ce0d06bf8", "e6ad213496394219b6c8f9a8a5cb85cc", "f1ded93a89584a979f7ab3242facc1e4", "1bada11b3852438ab55d47abdbfe3fe3", "4188d286415c479bbd505e14db84b427", "14cbe8eebb9642479da14f56b0b1be68", "5bc1c40d9cd4431cb7b404ba9ca4d71b", "19b87460cad9472082967747da65572d", "6a4f8de3102e407d8610755d65a1a212", "98af0d4d364241b5a90939ba83a57605", "0802889c878f49959c5f59f7ba4f3366", "41a38a62186c45bba314660f6bd97adf", "a5c4957113cd40bda770ea266ac42049", "359829d224e241afb4b9906835472405", "6de23ec856ab4a128bc71a78db21ad59", "1009326500fc42d3a6ab78728243a813", "d337c726a6724c13828ca88e00a5616f", "18836650897f4f27b391f7d8559843cc", "dd82e873fef741f68cd4e0bb4992d1dc", "46529fc4c5bf4e7997d4b7cecb0823dd", "75872f5317e64741a61b218dea8ffaba", "57abfbd055444c50b93002be098edd2f", "ad05714779f040d4a97e6021ea5ebf96", "2aa476a51004435897224cd0cf58bdba", "5b13a28a12e74364bd72255a895170a2", "5d8e965f9bda4863a1a57d2985235008", "64c52a2fcf3a476fb51595e915996089", "b10e5b4150cb43f49614c5b12cf828e6", "1ec9157495de454d8323a5f36d91cc05", "c8d0ff40d3cc4deab3f46f8373461144", "fcbfff192acc4f02a0bd901b37e02113", "c7e15193bea1470594fd79a12fd9ccca" ] }, "id": "XD_hg3Ooe2hn", "outputId": "65960391-0b0e-4959-84ca-06c20109f10c" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab_v3.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260627T234907_511420Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "xgrammar: ?\n", "\n", "loading 20 COCO val images …\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/40 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 3 — NATIVE-JSON CAPABILITY PROBE across the small-model ladder\n", "# Answers the investigation's headline question: which base models emit clean per-task\n", "# JSON *natively* (native_capable) vs need repair (finetune_candidate)? Also validates the\n", "# throughput fix (causal-conv1d fast path). Uses synthetic probe images, so it needs NO\n", "# ground-truth download — it measures JSON shape + robustness + speed, not accuracy.\n", "# Output is a compact table to paste back.\n", "#\n", "# Edit MODELS to add/remove rungs. Small first: 0.8B captioner vs 0.8B base shows what the\n", "# LoRA did to native JSON shape; 2B/4B/9B show whether bigger base models are clean natively.\n", "# ==========================================================================================\n", "import subprocess, time\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps (idempotent) + the fast-path libs that were MISSING in the CELL-2 run (8 tok/s).\n", "# On Colab Linux these build fine (unlike the Windows dev box).\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\")\n", "sh(\"pip -q install flash-linear-attention || echo 'fla install failed — ok'\")\n", "sh(\"pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\")\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit this list to choose the ladder rungs to probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"] # constrained needs xgrammar (falls back if missing)\n", "\n", "# ---- synthetic probe images (offline, deterministic) ----\n", "def _img_classification():\n", " return Image.new(\"RGB\", (256, 256), (40, 90, 160)) # a solid blue image\n", "\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)) # red box\n", " d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)) # blue box\n", " return im\n", "\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.text((40, 45), \"STOP\", fill=(0, 0, 0))\n", " return im\n", "\n", "PROBE_IMAGES = {\n", " \"image_classification\": _img_classification(),\n", " \"bbox_grounding\": _img_detection(),\n", " \"ocr_text\": _img_ocr(),\n", "}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, est {spec.est_vram_gb('bf16'):.1f}GB bf16) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(f\" LOAD FAILED: {type(e).__name__}: {str(e)[:200]}\")\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat)\n", " img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " snippet = res.raw_text.replace(\"\\n\", \" \")[:48]\n", " rows.append((mkey, cat, res.backend + \"/\" + mode,\n", " \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\",\n", " \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", snippet))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "# ---- compact, paste-friendly table ----\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ NATIVE-JSON CAPABILITY PROBE ================\\n\")\n", "print(line(hdr))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(line(r))\n", "\n", "# per-model native verdict (json_mode only = no grammar crutch)\n", "print(\"\\n---- native bucket (json_mode: emits clean valid JSON on its own?) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " if not jm:\n", " continue\n", " valid = sum(1 for r in jm if r[3] == \"Y\")\n", " robust = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if robust == len(jm) else (\"finetune_candidate\" if valid else \"insufficient/needs-look\")\n", " print(f\" {mkey:30s} valid {valid}/{len(jm)} robust {robust}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole PROBE block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 4 — FIX: turn ON constrained decoding (xgrammar) + stronger anti-fence prompts,\n", "# then re-run the probe.\n", "#\n", "# IMPORTANT: run this on a FRESH kernel. First do Runtime -> Restart runtime (this keeps\n", "# the downloaded models on disk, so no re-download). xgrammar must be installed BEFORE\n", "# qwen_test_runner is imported, or its constrained backend stays off.\n", "#\n", "# What changes vs CELL 3:\n", "# * installs xgrammar FIRST and shows any error (CELL 3 swallowed it -> constrained was a no-op)\n", "# * patches the 3 pilot prompts to forbid ``` fences and show the exact schema + the\n", "# 'box' key (so json_mode \"native\" numbers improve; mirrors the v2 zip)\n", "# * silences the noisy [transformers] processor-kwargs warning\n", "# With xgrammar live, the 'constrained' rows should show backend=xgrammar and flip to valid+robust.\n", "# ==========================================================================================\n", "import subprocess\n", "\n", "# ---- 1. install deps + xgrammar BEFORE importing qwen_test_runner ----\n", "def sh(c):\n", " print(\"\\n$ \" + c); subprocess.run(c, shell=True)\n", "\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed'\")\n", "print(\"\\n=== installing xgrammar (FULL output so we can see any error) ===\")\n", "subprocess.run(\"pip install xgrammar\", shell=True)\n", "try:\n", " import xgrammar as _xgr\n", " print(\"\\nxgrammar import OK — version\", getattr(_xgr, \"__version__\", \"?\"))\n", " XGRAMMAR = True\n", "except Exception as e:\n", " print(\"\\nxgrammar import FAILED:\", type(e).__name__, \"::\", str(e)[:400])\n", " XGRAMMAR = False\n", "\n", "# ---- 2. silence the benign [transformers] processor-kwargs spam ----\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers\n", " transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "# ---- 3. import the harness (AFTER xgrammar so its backend is detected) ----\n", "import time\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "# ---- 4. patch in the stronger anti-fence prompts (mirrors the v2 zip) ----\n", "_V2_PROMPTS = {\n", " \"image_classification\":\n", " \"You are an image classifier. Identify the single most prominent object or scene \"\n", " \"category in the image. Output ONLY a raw JSON object and NOTHING else — no prose, \"\n", " \"no explanation, and NO markdown code fences (do not wrap it in ```). It must match \"\n", " 'this shape exactly:\\n{\"label\": \"\", \"confidence\": , '\n", " '\"top5\": [{\"label\": \"\", \"score\": }]}',\n", " \"bbox_grounding\":\n", " \"You are an object detector. Find every distinct object in the image. Output ONLY a \"\n", " \"raw JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it \"\n", " 'in ```). It must match this shape exactly:\\n{\"detections\": [{\"label\": \"\", '\n", " '\"box\": [x1, y1, x2, y2], \"score\": }], \"count\": }\\n{coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with exactly four numbers [x1, y1, x2, y2].',\n", " \"ocr_text\":\n", " \"You are an OCR engine. Transcribe all readable text in the image. Output ONLY a raw \"\n", " \"JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it in \"\n", " '```). It must match this shape exactly:\\n{\"full_text\": \"\", '\n", " '\"lines\": [{\"text\": \"\", \"box\": [x1, y1, x2, y2]}]}\\n{coord_hint} '\n", " \"If you cannot localize a line, omit its box.\",\n", "}\n", "def _patched_resolved(spec):\n", " p = _V2_PROMPTS.get(spec.category)\n", " if p is None:\n", " return spec.system_prompt\n", " return p.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", "_R.resolved_system_prompt = _patched_resolved\n", "print(\"prompts patched (anti-fence + explicit schema). constrained backend will be:\",\n", " \"xgrammar\" if XGRAMMAR else \"json_mode FALLBACK (xgrammar missing)\")\n", "\n", "# ---- 5. re-run the probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40])); continue\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None) # confirms constrained is real\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, res.backend + \"/\" + mode, \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ PROBE v2 (constrained ON + anti-fence prompts) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (json_mode only) | constrained-valid (xgrammar forces schema) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " cn = [r for r in rows if r[0] == mkey and \"constrained\" in r[2]]\n", " if not jm: continue\n", " v_jm = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " v_cn = sum(1 for r in cn if r[3] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v_jm else \"needs-look\")\n", " print(f\" {mkey:30s} json_mode valid {v_jm}/{len(jm)} robust {rob}/{len(jm)} -> {bucket} \"\n", " f\"| constrained valid {v_cn}/{len(cn)}\")\n", "print(\"\\nPaste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 5 — BIGGER BRAINS: native-JSON probe across the larger ladder rungs (json_mode).\n", "# Run after CELL 4 (deps already installed; package has the anti-fence v2 prompts).\n", "# json_mode only = the native no-finetune signal. Clears each model's HF cache after use\n", "# so ~220GB of weights don't fill the 230GB SSD (set CLEAR_CACHE_AFTER=False to keep them).\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit these ----\n", "MODELS = [\"qwen3.5-9b\", \"qwen3.5-27b\", \"qwen3.5-35b-a3b\", \"qwen3vl-8b\", \"qwen3vl-32b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "CLEAR_CACHE_AFTER = True # delete each model's weights from disk after probing it\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", " print(\" cleared cache:\", p)\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, ~{spec.est_vram_gb('bf16'):.0f}GB, MoE={spec.is_moe}) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(\" LOAD FAILED:\", type(e).__name__, str(e)[:160])\n", " rows.append((mkey, \"-\", \"json_mode\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " try:\n", " res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=\"json_mode\", image_id=\"probe\",\n", " image_size=img.size, n_output_tokens=res.n_output_tokens,\n", " gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, \"json_mode\", \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, \"json_mode\", \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", "\n", "hdr = [\"model\", \"category\", \"mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ BIGGER-BRAINS PROBE (json_mode native) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (clean valid JSON on its own, across all 3 categories) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[3] in (\"Y\", \"n\")]\n", " if not jm:\n", " print(f\" {mkey:18s} (no rows / load failed)\"); continue\n", " v = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v else \"needs-look\")\n", " print(f\" {mkey:18s} valid {v}/{len(jm)} robust {rob}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole BIGGER-BRAINS block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 6 — REAL GROUND TRUTH: COCO detection + coordinate-scale CALIBRATION.\n", "# Before trusting any IoU, we must know what coordinate scale the model emits. This cell\n", "# runs qwen3-vl-8b (the native grounding champion) on real COCO val images and scores\n", "# detection under ALL 3 coord spaces x BOTH box formats — the combo with the best F1\n", "# reveals the model's true convention. It also prints raw model output + GT so we can see.\n", "#\n", "# Run on a fresh-ish kernel OR after CELL 4/5 (needs: datasets, the harness, a GPU).\n", "# ==========================================================================================\n", "import warnings, itertools\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from datasets import load_dataset\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task, model_for\n", "from qwen_test_runner.vision.metrics import score_detection\n", "from qwen_test_runner.vision.coords import CoordSpace, XYWH, XYXY\n", "from qwen_test_runner.evaluator import parse_against\n", "\n", "N = 10\n", "MODEL = \"qwen3vl-8b\"\n", "\n", "# ---- load N COCO val samples (streaming) and SHOW the schema so we can see GT format ----\n", "def _load_coco(n):\n", " last_err = None\n", " for repo, split in [(\"detection-datasets/coco\", \"val\"), (\"detection-datasets/coco\", \"validation\"),\n", " (\"rafaelpadilla/coco2017\", \"val\")]:\n", " try:\n", " ds = load_dataset(repo, split=split, streaming=True)\n", " rows = list(itertools.islice(ds, n))\n", " print(f\"loaded {len(rows)} from {repo}:{split}\")\n", " return rows, ds\n", " except Exception as e:\n", " last_err = e\n", " print(f\" {repo}:{split} failed -> {type(e).__name__}: {str(e)[:120]}\")\n", " raise last_err\n", "\n", "rows, ds = _load_coco(N)\n", "print(\"\\n=== dataset features ===\")\n", "try:\n", " print(ds.features)\n", "except Exception as e:\n", " print(\"features unavailable:\", e)\n", "print(\"\\n=== first sample objects (raw) ===\")\n", "print({k: (v if k != \"image\" else f\"\") for k, v in rows[0].items()})\n", "\n", "# ---- category id -> name map (best effort) ----\n", "cat_names = None\n", "try:\n", " obj_feat = ds.features[\"objects\"]\n", " inner = obj_feat.feature if hasattr(obj_feat, \"feature\") else obj_feat\n", " cat_feat = inner[\"category\"] if isinstance(inner, dict) else None\n", " cat_names = getattr(getattr(cat_feat, \"feature\", cat_feat), \"names\", None)\n", "except Exception as e:\n", " print(\"cat-name lookup failed:\", e)\n", "def _cat(cid):\n", " if cat_names and isinstance(cid, int) and 0 <= cid < len(cat_names):\n", " return cat_names[cid]\n", " return str(cid)\n", "\n", "# ---- build GT samples from whatever schema we got ----\n", "def _gt_from(sample):\n", " img = sample[\"image\"]\n", " objs = sample.get(\"objects\", {})\n", " bboxes = objs.get(\"bbox\") or objs.get(\"bboxes\") or []\n", " cats = objs.get(\"category\") or objs.get(\"categories\") or objs.get(\"label\") or []\n", " boxes = [{\"label\": str(_cat(c)).lower().replace(\"_\", \" \"), \"bbox\": list(map(float, b))}\n", " for b, c in zip(bboxes, cats)]\n", " return img, boxes, img.size\n", "\n", "samples = [_gt_from(r) for r in rows]\n", "print(f\"\\nfirst GT boxes: {samples[0][1][:3]} | image size {samples[0][2]}\")\n", "\n", "# ---- run the model ----\n", "spec = get_model(MODEL)\n", "task = get_task(\"bbox_grounding\")\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", "preds = []\n", "for i, (img, boxes, size) in enumerate(samples):\n", " res = runner.generate(task, img, \"json_mode\", image_id=f\"coco{i}\", image_size=size)\n", " preds.append(res.raw_text)\n", " if i < 2:\n", " print(f\"\\n--- sample {i} (size {size}) RAW MODEL OUTPUT ---\\n{res.raw_text[:400]}\")\n", " print(f\"--- sample {i} GT boxes (pixel) ---\\n{boxes[:4]}\")\n", "runner.close()\n", "\n", "# ---- CALIBRATE: score detection under every coord_space x box-fmt; best F1 = the convention ----\n", "Model = model_for(task)\n", "print(\"\\n================ COORDINATE CALIBRATION (mean detection F1) ================\")\n", "print(f\"{'coord_space':14s} {'gt_fmt':6s} meanF1 (over {N} images)\")\n", "best = (None, None, -1.0)\n", "for space in [CoordSpace.NORM_0_1000, CoordSpace.NORM_0_1, CoordSpace.PIXEL_ABS]:\n", " for fmt in [XYWH, XYXY]:\n", " f1s = []\n", " for (img, boxes, size), raw in zip(samples, preds):\n", " pr = parse_against(raw, Model)\n", " if not pr.schema_valid:\n", " f1s.append(0.0); continue\n", " gt = {\"boxes\": [{\"label\": b[\"label\"], \"bbox\": b[\"bbox\"], \"fmt\": fmt} for b in boxes]}\n", " ctx = {\"size\": size, \"coord_space\": space}\n", " try:\n", " primary, _ = score_detection(pr.parsed.model_dump(), gt, ctx)\n", " f1s.append(primary or 0.0)\n", " except Exception:\n", " f1s.append(0.0)\n", " mean = sum(f1s) / len(f1s) if f1s else 0.0\n", " print(f\"{space.value:14s} {fmt:6s} {mean:.3f}\")\n", " if mean > best[2]:\n", " best = (space.value, fmt, mean)\n", "print(f\"\\nBEST: coord_space={best[0]} gt_fmt={best[1]} meanF1={best[2]:.3f}\")\n", "print(\"(Low across the board can mean labels mismatch COCO names, not coords — check the RAW vs GT above.)\")\n", "print(\"\\nPaste this whole CELL-6 block back (especially the RAW outputs, GT boxes, and the calibration table).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 7 — REAL DETECTION ACCURACY (v3): native models on real COCO, with the upgraded\n", "# scorer (tolerant labels + class-agnostic localization).\n", "#\n", "# REQUIRES THE v3 ZIP. Do: Runtime -> Restart runtime, drag qwen_vlmbench_colab_v3.zip\n", "# into /content, re-run CELL 1 (it re-extracts + reinstalls), THEN run this cell.\n", "# (Restart matters so the new package + xgrammar-before-import both take effect.)\n", "#\n", "# Reports per model: labeled F1 (tv==television etc.), class-agnostic LOCALIZATION F1\n", "# (can it box objects at all), recall, count error, tok/s. Clears HF cache between models.\n", "# ==========================================================================================\n", "import os, shutil, warnings, subprocess\n", "warnings.filterwarnings(\"ignore\")\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "# xgrammar BEFORE importing the harness so v3's lazy detection turns constrained ON\n", "subprocess.run(\"pip -q install xgrammar\", shell=True)\n", "try:\n", " import xgrammar; print(\"xgrammar:\", getattr(xgrammar, \"__version__\", \"?\"))\n", "except Exception as e:\n", " print(\"xgrammar not available:\", e)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from qwen_test_runner.vision.datasets import load_coco_detection\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"] # native champions; edit freely\n", "N = 20\n", "MODES = [\"json_mode\"] # add \"constrained\" to test the grammar path (v3 lazy-xgrammar)\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "print(f\"\\nloading {N} COCO val images …\")\n", "samples = load_coco_detection(n=N, split=\"val\")\n", "print(f\"got {len(samples)} samples; first GT: {samples[0].gt['boxes'][:2]} size={samples[0].size}\")\n", "\n", "spec = get_task(\"bbox_grounding\")\n", "summary = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} ({ms.repo_id}) #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None)\n", " try:\n", " for mode in MODES:\n", " results = []\n", " for s in samples:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " results.append((mr, res))\n", " if s is samples[0]:\n", " print(f\" [{mode}] sample0 raw: {res.raw_text[:160]}\")\n", " mrs = [r[0] for r in results]\n", " run = score_vision_run(mrs, model=mkey, category=\"bbox_grounding\", mode=mode)\n", " loc = sum(r.metrics.get(\"localization_f1\", 0.0) for r in mrs if r.schema_valid)\n", " rec = sum(r.metrics.get(\"recall\", 0.0) for r in mrs if r.schema_valid)\n", " cerr = sum(r.metrics.get(\"count_abs_err\", 0.0) for r in mrs if r.schema_valid)\n", " nv = sum(1 for r in mrs if r.schema_valid) or 1\n", " summary.append({\n", " \"model\": mkey, \"mode\": mode, \"backend\": results[0][1].backend,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"labeledF1\": run.primary_score_mean or 0.0,\n", " \"localF1\": loc / nv, \"recall\": rec / nv, \"count_err\": cerr / nv,\n", " \"tok_s\": run.tokens_per_sec,\n", " })\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "print(\"\\n================ REAL COCO DETECTION (N=%d) ================\\n\" % N)\n", "hdr = [\"model\", \"mode\", \"backend\", \"valid\", \"robust\", \"labeledF1\", \"localF1\", \"recall\", \"count_err\", \"tok/s\"]\n", "def fmt(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(fmt(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + summary)) for h in hdr]\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in summary:\n", " print(\" \".join(fmt(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nlabeledF1 = boxes matched WITH a tolerant label (tv==television); \"\n", " \"localF1 = boxes matched ignoring labels (pure localization).\")\n", "print(\"Paste this whole CELL-7 block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 8 — DECISIVE: json_mode vs CONSTRAINED on real COCO (recovers the lost validity).\n", "# Self-contained; runs in the current v3 kernel (xgrammar already active). No re-drag.\n", "# Fixes the two validity killers from CELL 7: (1) bumps max_new_tokens 768->2048 to stop\n", "# truncation, (2) tightens the prompt against bare top-level arrays. Then compares\n", "# json_mode (prompt-only) vs constrained (grammar-FORCED object) — the grammar makes a\n", "# bare array / fence / truncated-invalid impossible, so validity should jump.\n", "# ==========================================================================================\n", "import os, shutil, warnings, dataclasses\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from qwen_test_runner.vision.datasets import load_coco_detection\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "N = 20\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "# stronger bbox prompt (single OBJECT, not a bare array; bounded count). Patch the runner's\n", "# resolver so it applies in-place without a re-drag; non-bbox categories keep their prompt.\n", "_BBOX_PROMPT = (\n", " \"You are an object detector. Find the distinct objects in the image. Output ONLY a single \"\n", " \"raw JSON OBJECT — NOT a bare array, NO markdown code fences. It must match exactly:\\n\"\n", " '{\"detections\": [{\"label\": \"\", \"box\": [x1, y1, x2, y2], \"score\": }], '\n", " '\"count\": }\\nReport the most prominent objects, up to 30. {coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with four numbers [x1, y1, x2, y2].'\n", ")\n", "def _resolved(spec):\n", " if spec.category == \"bbox_grounding\":\n", " return _BBOX_PROMPT.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", " return spec.system_prompt\n", "_R.resolved_system_prompt = _resolved\n", "\n", "spec = dataclasses.replace(get_task(\"bbox_grounding\"), max_new_tokens=2048)\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "print(f\"loading {N} COCO val images …\")\n", "samples = load_coco_detection(n=N, split=\"val\")\n", "\n", "summary = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None)\n", " try:\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " try:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " except Exception as e:\n", " print(f\" {mode} {s.image_id}: ERR {type(e).__name__}: {str(e)[:80]}\"); continue\n", " mrs.append(mr)\n", " if s is samples[0]:\n", " print(f\" [{mode}] backend={res.backend} sample0 raw: {res.raw_text[:140]}\")\n", " if not mrs:\n", " continue\n", " run = score_vision_run(mrs, model=mkey, category=\"bbox_grounding\", mode=mode)\n", " nv = sum(1 for r in mrs if r.schema_valid) or 1\n", " summary.append({\n", " \"model\": mkey, \"mode\": mode, \"backend\": mrs and \"mix\",\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"labeledF1\": run.primary_score_mean or 0.0,\n", " \"localF1\": sum(r.metrics.get(\"localization_f1\", 0.0) for r in mrs if r.schema_valid) / nv,\n", " \"recall\": sum(r.metrics.get(\"recall\", 0.0) for r in mrs if r.schema_valid) / nv,\n", " \"tok/s\": run.tokens_per_sec,\n", " })\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"mode\", \"valid\", \"robust\", \"labeledF1\", \"localF1\", \"recall\", \"tok/s\"]\n", "def _fmt(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(_fmt(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + summary)) for h in hdr]\n", "print(\"\\n================ json_mode vs CONSTRAINED on real COCO (N=%d) ================\\n\" % N)\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in summary:\n", " print(\" \".join(_fmt(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nIf constrained lifts 'valid' toward 20/20 while json_mode stays ~half, the grammar fallback works:\")\n", "print(\"a capable model + grammar = robust labeler. Paste this whole CELL-8 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "e7d0fb4b76ab44fd93f8c6a966ce5b8d", "e738416125a24713a05ce2642bc525ea", "273a4fa5ec5140b9980f7724c12f5c64", "63a65776676d4121b5a5e26a80192a61", "b1b575492f644d799459c9c6aba73973", "659d9a58850740f0b772fa5eafcdc67d", "72edaf0e24384336bf834ccf9bdf918d", "31125cdb77624807b7f124cbb86a5b2b", "3119a704cb9147aeae83bd123c4724d5", "068ebcbd4c5547f9a1c3363babe93ff0", "3668c55a08a14f66b5a03bf094caa4ff", "caea15341fa6438cab6504928175dce3", "a93bd8162ad04c4ea33accc3e730ef39", "366d29b49dc94240ac35dbad34efd915", "400268300d4d47c3a659622a76a6067a", "4b593b290a814994a976ec543b94d595", "0b6a9fca8e6c40e692585b4acdf8330b", "dfb7a62b11774337a4b7ddc7c8eca803", "f16bd3dca4564c7b8c9f516e7e46fb6b", "ba7505886a8e421e952258682280f21c", "8f4125ffd3114dd29cd24146bd964a72", "60c7d312389b4e929d8a041d3e5233b7", "a6915ccc12ee46d0980fa39d746fd979", 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"8c478ad0f3de4fc6b5bd611748e6a8b9", "05d164ad4f574fb6a1ab225dee8f9e9e", "172b8514d6f94ec9859d37c67f241862", "61005ee003d94aabbaf29b044b081e2b", "bcc1a53ecf3f4d75a784cc44628f3458", "624545df859c46fc89e9d5c89090926e", "10ae8b4c5fd346f9957081f027762169", "82c4a9030a2d45b781fad593afd72f3d", "2b49c8fa24894f9da66194d3946e1a41", "4d0a3724215e4000b0639a6d0a8816cc", "e492e1007ea94eeeaed2305533f6b22c", "382ceeefa1a7453b863a8e0bdd75a921", "4fc22292705c4cfc9d4d66ddbe61467c", "401f441d5d75426f8f93086933288099", "ccdcd9d95ba74c37b0ed8b9ef592e5c9", "47673a0239fc4579928da710f29059ac", "6cdd93751eb6497588499a492b518499", "c2acfa092d6e4e6581e40bc77ff80330", "8dd14e9c92d74e84a5349624d07508c8", "c8672efdfdfe4095b2dba4e7ca45f905", "450f0f8620924527a2a1d8ff59351f20", "cea24ef0caf74f47b8e475dc38d0db08", "44abe5e7630747f396bddaac8c290450", "5358eb4183194398b6e36ba9b3d7e88f", "84499dc4d10d48069542f90d65eb4af4", "80b3f50e42424e6f99039685166d21b8", "c1adc366ac9248a9a815be234845a021", "f9df9f6befc544acb11fe51001c0a746", "39e2c452a5d6469fb125a7c131a05eed", "b2642c3558424b6f83cbce1496fe7239", "49550c081ada4a85aa70de75ff002b08", "407dc17352db4d0b9c66dcad12c64ebe", "94faef3781c84e9ab19e6bb5b0a811e2", "a111bebf94ea464b988eaa2ae29494e7", "dfda1679da2b4a9da76edd7363848b74", "c65f6d0819ec43e1bd4d7adc63d1a11d", "efa7df254ac24ca886fab8f22a869b09", "3250e512036f4440af1da40aa4f1021b", "d46b1b9d74d946e690217c1e6f3ccdae", "a0a90096bfde4e5080b26226e1cd989e", "3d01b722553c41a886c1b7767a218632", "f0843c1dc1ba4f1383ae8c48c3503559", "e24875e6c89c406c8d53b5f9fff11527", "f96296f8ed1c4766850a11b325d3a9f1", "a318af572cd2418da85b122ae053a51b", "89a08679af384451bd610b0dd18d5ccc", "91e2859f2c5a4f6894d35c544e3b43cf", "b7749a3825e94d28b970a5eecd9c9c1e", "8b984a4f49794fcba3129e1473b4886f", "fcfae0921ad6433985248d5dd154d7e5", "4137fb2c3bc74c199303651f9d1ff567", "d940696496f0409abd24882c2caf336b", "f86674962d26483b9b7a8c10d6b6e080", "22ea556c4f3f47d495e1a09449b573d0", "cdd79a7c11dc44c8bdde04885775f080", "2d6e9c4ec5c341bfb615417e854a9d1f", "c6e69c70d25b42c4b63c05e52688c92f", "3c3b585f577c42eea1f44930248a8a80", "5c01781982214d3588f4c9f9f505e8e9", "043c6a7dd42a46a3a39e9e6617b2b0e5", "ee5a8e813b8d4638807eb3fc363d9cb5", "fde540202b704350ba6b04b8eb522d08", "6e0359b2c7714dfcb6f6109a89dfe98d", "ab41cdd88fef4945add8b5f00ed5d3ed", "250a1d33a33d44b9b68b158414227be2", "1ae2ba9497224911968d8aa1ccb77efd", "6c541d5dc00241b8b7d0d208e9985a6b", "07625a1a2c2842aabbdb26d08e18400d", "d14a21f0aa5f48d7bc8dffa2435a3c16", "c8b28b94ea2d4e998c4c62e8b339d786", "378aca6800874f81807d2bc7769fa311", "25848b23dff9446abc7e188522ab40da", "ff9532a888ee42518c84eab8bb5b1d4d", "fb89b84c3b6e4a04a8205a2d2a383b01", "3f41815e0d924ce48c4cb98e680aeb77", "4b8ef8e1a12c4037baaee720eff1e1c8", "1fea475c901248078499e91a612433a9" ] }, "id": "1eJg47ADlAEM", "outputId": "46466c11-a90a-4654-ac1a-8435e6f13cef" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: /content/qwen_vlmbench_colab_v3.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260628T001457_339480Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00\n", "\n", "\n", "\n", "{\"label\": \"blue color\", \"confidence\": 1.0}\n", "user\n", "What is the most prominent object or scene category in the image?\n", "Answer: blue color\n", "assistant\n", "\n", "\n", "\n", "{\"label\": \"blue color\", \"confidence\": 1.0}\n", "\n", "\n", "schema_valid: True | needed_repair: True | json_robust: False | out_tokens: 109 | gen_seconds: 1.29\n", "REAL VLMRunner PATH WORKS.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\n", "\n", "$ pip -q install flash-linear-attention || echo 'fla install failed — ok'\n", "\n", "$ pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\n", "\n", "##### loading qwen3.5-0.8b-json-captioner (AbstractPhil/Qwen3.5-0.8B-json-captioner, est 1.7GB bf16) #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate\n", " qwen3.5-0.8b valid 1/3 robust 1/3 -> finetune_candidate\n", " qwen3.5-2b valid 2/3 robust 2/3 -> finetune_candidate\n", " qwen3.5-4b valid 3/3 robust 3/3 -> native_capable\n", "\n", "Paste this whole PROBE block back.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed'\n", "\n", "=== installing xgrammar (FULL output so we can see any error) ===\n", "\n", "xgrammar import OK — version ?\n", "prompts patched (anti-fence + explicit schema). constrained backend will be: xgrammar\n", "\n", "##### loading qwen3.5-0.8b-json-captioner #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate | constrained valid 2/3\n", " qwen3.5-0.8b json_mode valid 1/3 robust 1/3 -> finetune_candidate | constrained valid 3/3\n", " qwen3.5-2b json_mode valid 2/3 robust 2/3 -> finetune_candidate | constrained valid 3/3\n", " qwen3.5-4b json_mode valid 3/3 robust 3/3 -> native_capable | constrained valid 3/3\n", "\n", "Paste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\n", "\n", "##### loading qwen3.5-9b (Qwen/Qwen3.5-9B, ~18GB, MoE=False) #####\n", "[VLMRunner] loading Qwen/Qwen3.5-9B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00 native_capable\n", " qwen3.5-27b valid 3/3 robust 3/3 -> native_capable\n", " qwen3.5-35b-a3b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-8b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-32b valid 3/3 robust 3/3 -> native_capable\n", "\n", "Paste this whole BIGGER-BRAINS block back.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/40 [00:00', 'width': 640, 'height': 426, 'objects': {'bbox_id': [26547, 34646, 35802, 103487, 104368, 105328, 110334, 230831, 233201, 1640282, 1647285, 1648594, 1654394, 1666628, 1667817, 1669970, 1941808, 2146194, 2146548, 2204286], 'category': [58, 62, 62, 56, 56, 56, 56, 0, 0, 68, 72, 73, 73, 74, 75, 75, 56, 75, 75, 60], 'bbox': [[236.98, 142.51, 261.68, 212.01], [7.03, 167.76, 156.35, 262.63], [557.21, 209.19, 638.5600000000001, 287.92], [358.98, 218.05, 414.98, 320.88], [290.69, 218.0, 352.52, 316.48], [413.2, 223.01, 443.37, 304.37], [317.4, 219.24, 338.97999999999996, 230.83], [412.8, 157.61, 465.85, 295.62], [384.43, 172.21, 399.55, 207.95000000000002], [512.22, 205.75, 526.96, 221.72], [493.1, 174.34, 513.39, 282.65], [604.77, 305.89, 619.11, 351.59999999999997], [613.24, 308.24, 626.12, 354.68], [447.77, 121.12, 461.74, 143.0], [549.06, 309.43, 585.7399999999999, 399.1], [350.76, 208.84, 362.13, 231.39000000000001], [412.25, 219.02, 421.88, 231.54000000000002], [241.24, 194.99, 255.46, 212.62], [336.79, 199.5, 346.52000000000004, 216.23], [321.21, 231.22, 446.77, 320.15]], 'area': [531.8071000000001, 13244.657700000002, 5833.117949999999, 2245.34355, 1833.7840000000017, 1289.3734500000014, 210.14820000000023, 2913.1103999999987, 435.1449499999997, 217.71919999999997, 2089.9747999999986, 338.60884999999973, 322.5935999999982, 225.6642000000005, 2171.6188500000007, 178.18510000000012, 90.98724999999988, 189.56010000000012, 120.23200000000004, 2362.4897499999984]}}\n", "\n", "first GT boxes: [{'label': 'potted plant', 'bbox': [236.98, 142.51, 261.68, 212.01]}, {'label': 'tv', 'bbox': [7.03, 167.76, 156.35, 262.63]}, {'label': 'tv', 'bbox': [557.21, 209.19, 638.5600000000001, 287.92]}] | image size (640, 426)\n", "[VLMRunner] loading Qwen/Qwen3-VL-8B-Instruct (image_text_to_text, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 2 — (OPTIONAL, heavier) LOAD A REAL QWEN VLM AND RUN THE PIPELINE\n", "# Installs the full vision stack (transformers@main, qwen-vl-utils) and runs the\n", "# 0.8B json-captioner baseline through the real VLMRunner on a generated image.\n", "# This proves the real load+generate+score path works on the 6000. Swap MODEL_KEY\n", "# for any key in qwen_test_runner/vision/model_registry.py to bench a bigger model.\n", "# ==========================================================================================\n", "import subprocess\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps for real model loading (Qwen3.5 multimodal needs transformers main)\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow\")\n", "# optional grammar-constrained decoding backend (builds in ~1-2 min):\n", "# sh(\"pip -q install xgrammar\")\n", "\n", "from PIL import Image\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "MODEL_KEY = \"qwen3.5-0.8b-json-captioner\" # the LoRA-merged baseline (small, fast download)\n", "spec = get_model(MODEL_KEY)\n", "print(\"loading\", spec.repo_id, \"| est VRAM bf16:\", spec.est_vram_gb(\"bf16\"), \"GB\")\n", "\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", "\n", "# a synthetic image — this is a plumbing smoke (we care that it LOADS, emits JSON, and\n", "# the pipeline scores it; accuracy on a blank image is not meaningful).\n", "img = Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "task = get_task(\"image_classification\")\n", "res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", "print(\"\\nRAW MODEL OUTPUT:\\n\", res.raw_text[:1000])\n", "\n", "mr = score_vision_sample(task, res.raw_text, {\"labels\": [\"unknown\"]}, mode=\"json_mode\",\n", " image_id=\"probe\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", "print(\"\\nschema_valid:\", mr.schema_valid, \"| needed_repair:\", mr.needed_repair,\n", " \"| json_robust:\", mr.json_robust,\n", " \"| out_tokens:\", res.n_output_tokens, \"| gen_seconds:\", round(res.gen_seconds, 2))\n", "print(\"REAL VLMRunner PATH WORKS.\" if mr.schema_valid\n", " else \"Model loaded + generated; JSON did not validate — inspect RAW OUTPUT above.\")\n", "runner.close()\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 3 — NATIVE-JSON CAPABILITY PROBE across the small-model ladder\n", "# Answers the investigation's headline question: which base models emit clean per-task\n", "# JSON *natively* (native_capable) vs need repair (finetune_candidate)? Also validates the\n", "# throughput fix (causal-conv1d fast path). Uses synthetic probe images, so it needs NO\n", "# ground-truth download — it measures JSON shape + robustness + speed, not accuracy.\n", "# Output is a compact table to paste back.\n", "#\n", "# Edit MODELS to add/remove rungs. Small first: 0.8B captioner vs 0.8B base shows what the\n", "# LoRA did to native JSON shape; 2B/4B/9B show whether bigger base models are clean natively.\n", "# ==========================================================================================\n", "import subprocess, time\n", "def sh(c):\n", " print(\"\\n$ \" + c)\n", " subprocess.run(c, shell=True)\n", "\n", "# full deps (idempotent) + the fast-path libs that were MISSING in the CELL-2 run (8 tok/s).\n", "# On Colab Linux these build fine (unlike the Windows dev box).\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\")\n", "sh(\"pip -q install flash-linear-attention || echo 'fla install failed — ok'\")\n", "sh(\"pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\")\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit this list to choose the ladder rungs to probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"] # constrained needs xgrammar (falls back if missing)\n", "\n", "# ---- synthetic probe images (offline, deterministic) ----\n", "def _img_classification():\n", " return Image.new(\"RGB\", (256, 256), (40, 90, 160)) # a solid blue image\n", "\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)) # red box\n", " d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)) # blue box\n", " return im\n", "\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255))\n", " d = ImageDraw.Draw(im)\n", " d.text((40, 45), \"STOP\", fill=(0, 0, 0))\n", " return im\n", "\n", "PROBE_IMAGES = {\n", " \"image_classification\": _img_classification(),\n", " \"bbox_grounding\": _img_detection(),\n", " \"ocr_text\": _img_ocr(),\n", "}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, est {spec.est_vram_gb('bf16'):.1f}GB bf16) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind,\n", " precision=\"bf16\", device_map=\"cuda\",\n", " enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(f\" LOAD FAILED: {type(e).__name__}: {str(e)[:200]}\")\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat)\n", " img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " snippet = res.raw_text.replace(\"\\n\", \" \")[:48]\n", " rows.append((mkey, cat, res.backend + \"/\" + mode,\n", " \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\",\n", " \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", snippet))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "# ---- compact, paste-friendly table ----\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ NATIVE-JSON CAPABILITY PROBE ================\\n\")\n", "print(line(hdr))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(line(r))\n", "\n", "# per-model native verdict (json_mode only = no grammar crutch)\n", "print(\"\\n---- native bucket (json_mode: emits clean valid JSON on its own?) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " if not jm:\n", " continue\n", " valid = sum(1 for r in jm if r[3] == \"Y\")\n", " robust = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if robust == len(jm) else (\"finetune_candidate\" if valid else \"insufficient/needs-look\")\n", " print(f\" {mkey:30s} valid {valid}/{len(jm)} robust {robust}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole PROBE block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 4 — FIX: turn ON constrained decoding (xgrammar) + stronger anti-fence prompts,\n", "# then re-run the probe.\n", "#\n", "# IMPORTANT: run this on a FRESH kernel. First do Runtime -> Restart runtime (this keeps\n", "# the downloaded models on disk, so no re-download). xgrammar must be installed BEFORE\n", "# qwen_test_runner is imported, or its constrained backend stays off.\n", "#\n", "# What changes vs CELL 3:\n", "# * installs xgrammar FIRST and shows any error (CELL 3 swallowed it -> constrained was a no-op)\n", "# * patches the 3 pilot prompts to forbid ``` fences and show the exact schema + the\n", "# 'box' key (so json_mode \"native\" numbers improve; mirrors the v2 zip)\n", "# * silences the noisy [transformers] processor-kwargs warning\n", "# With xgrammar live, the 'constrained' rows should show backend=xgrammar and flip to valid+robust.\n", "# ==========================================================================================\n", "import subprocess\n", "\n", "# ---- 1. install deps + xgrammar BEFORE importing qwen_test_runner ----\n", "def sh(c):\n", " print(\"\\n$ \" + c); subprocess.run(c, shell=True)\n", "\n", "sh(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\")\n", "sh(\"pip -q install causal-conv1d || echo 'causal-conv1d build failed'\")\n", "print(\"\\n=== installing xgrammar (FULL output so we can see any error) ===\")\n", "subprocess.run(\"pip install xgrammar\", shell=True)\n", "try:\n", " import xgrammar as _xgr\n", " print(\"\\nxgrammar import OK — version\", getattr(_xgr, \"__version__\", \"?\"))\n", " XGRAMMAR = True\n", "except Exception as e:\n", " print(\"\\nxgrammar import FAILED:\", type(e).__name__, \"::\", str(e)[:400])\n", " XGRAMMAR = False\n", "\n", "# ---- 2. silence the benign [transformers] processor-kwargs spam ----\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers\n", " transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "# ---- 3. import the harness (AFTER xgrammar so its backend is detected) ----\n", "import time\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "# ---- 4. patch in the stronger anti-fence prompts (mirrors the v2 zip) ----\n", "_V2_PROMPTS = {\n", " \"image_classification\":\n", " \"You are an image classifier. Identify the single most prominent object or scene \"\n", " \"category in the image. Output ONLY a raw JSON object and NOTHING else — no prose, \"\n", " \"no explanation, and NO markdown code fences (do not wrap it in ```). It must match \"\n", " 'this shape exactly:\\n{\"label\": \"\", \"confidence\": , '\n", " '\"top5\": [{\"label\": \"\", \"score\": }]}',\n", " \"bbox_grounding\":\n", " \"You are an object detector. Find every distinct object in the image. Output ONLY a \"\n", " \"raw JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it \"\n", " 'in ```). It must match this shape exactly:\\n{\"detections\": [{\"label\": \"\", '\n", " '\"box\": [x1, y1, x2, y2], \"score\": }], \"count\": }\\n{coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with exactly four numbers [x1, y1, x2, y2].',\n", " \"ocr_text\":\n", " \"You are an OCR engine. Transcribe all readable text in the image. Output ONLY a raw \"\n", " \"JSON object and NOTHING else — no prose, no markdown code fences (do not wrap it in \"\n", " '```). It must match this shape exactly:\\n{\"full_text\": \"\", '\n", " '\"lines\": [{\"text\": \"\", \"box\": [x1, y1, x2, y2]}]}\\n{coord_hint} '\n", " \"If you cannot localize a line, omit its box.\",\n", "}\n", "def _patched_resolved(spec):\n", " p = _V2_PROMPTS.get(spec.category)\n", " if p is None:\n", " return spec.system_prompt\n", " return p.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", "_R.resolved_system_prompt = _patched_resolved\n", "print(\"prompts patched (anti-fence + explicit schema). constrained backend will be:\",\n", " \"xgrammar\" if XGRAMMAR else \"json_mode FALLBACK (xgrammar missing)\")\n", "\n", "# ---- 5. re-run the probe ----\n", "MODELS = [\"qwen3.5-0.8b-json-captioner\", \"qwen3.5-0.8b\", \"qwen3.5-2b\", \"qwen3.5-4b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " rows.append((mkey, \"-\", \"-\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40])); continue\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None) # confirms constrained is real\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " for mode in MODES:\n", " try:\n", " res = runner.generate(task, img, mode, image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=mode, image_id=\"probe\",\n", " image_size=img.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, res.backend + \"/\" + mode, \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, mode, \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", "\n", "hdr = [\"model\", \"category\", \"backend/mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ PROBE v2 (constrained ON + anti-fence prompts) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (json_mode only) | constrained-valid (xgrammar forces schema) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[2].endswith(\"/json_mode\")]\n", " cn = [r for r in rows if r[0] == mkey and \"constrained\" in r[2]]\n", " if not jm: continue\n", " v_jm = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " v_cn = sum(1 for r in cn if r[3] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v_jm else \"needs-look\")\n", " print(f\" {mkey:30s} json_mode valid {v_jm}/{len(jm)} robust {rob}/{len(jm)} -> {bucket} \"\n", " f\"| constrained valid {v_cn}/{len(cn)}\")\n", "print(\"\\nPaste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 5 — BIGGER BRAINS: native-JSON probe across the larger ladder rungs (json_mode).\n", "# Run after CELL 4 (deps already installed; package has the anti-fence v2 prompts).\n", "# json_mode only = the native no-finetune signal. Clears each model's HF cache after use\n", "# so ~220GB of weights don't fill the 230GB SSD (set CLEAR_CACHE_AFTER=False to keep them).\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from PIL import Image, ImageDraw\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample\n", "\n", "# ---- edit these ----\n", "MODELS = [\"qwen3.5-9b\", \"qwen3.5-27b\", \"qwen3.5-35b-a3b\", \"qwen3vl-8b\", \"qwen3vl-32b\"]\n", "CATEGORIES = [\"image_classification\", \"bbox_grounding\", \"ocr_text\"]\n", "CLEAR_CACHE_AFTER = True # delete each model's weights from disk after probing it\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", " print(\" cleared cache:\", p)\n", "\n", "def _img_classification(): return Image.new(\"RGB\", (256, 256), (40, 90, 160))\n", "def _img_detection():\n", " im = Image.new(\"RGB\", (320, 240), (255, 255, 255)); d = ImageDraw.Draw(im)\n", " d.rectangle([20, 20, 90, 110], fill=(220, 30, 30)); d.rectangle([180, 120, 270, 210], fill=(30, 30, 220)); return im\n", "def _img_ocr():\n", " im = Image.new(\"RGB\", (240, 120), (255, 255, 255)); ImageDraw.Draw(im).text((40, 45), \"STOP\", fill=(0, 0, 0)); return im\n", "PROBE_IMAGES = {\"image_classification\": _img_classification(), \"bbox_grounding\": _img_detection(), \"ocr_text\": _img_ocr()}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " spec = get_model(mkey)\n", " print(f\"\\n##### loading {mkey} ({spec.repo_id}, ~{spec.est_vram_gb('bf16'):.0f}GB, MoE={spec.is_moe}) #####\")\n", " try:\n", " runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\", enable_thinking=spec.enable_thinking_flag(\"instruct\"))\n", " except Exception as e:\n", " print(\" LOAD FAILED:\", type(e).__name__, str(e)[:160])\n", " rows.append((mkey, \"-\", \"json_mode\", \"LOAD_FAIL\", \"\", \"\", \"\", \"\", str(e)[:40]))\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", " continue\n", " try:\n", " for cat in CATEGORIES:\n", " task = get_task(cat); img = PROBE_IMAGES[cat]\n", " try:\n", " res = runner.generate(task, img, \"json_mode\", image_id=\"probe\", image_size=img.size)\n", " mr = score_vision_sample(task, res.raw_text, None, mode=\"json_mode\", image_id=\"probe\",\n", " image_size=img.size, n_output_tokens=res.n_output_tokens,\n", " gen_seconds=res.gen_seconds)\n", " toks = res.n_output_tokens / res.gen_seconds if res.gen_seconds > 0 else 0.0\n", " rows.append((mkey, cat, \"json_mode\", \"Y\" if mr.schema_valid else \"n\",\n", " \"Y\" if mr.json_robust else \"n\", \"Y\" if mr.needed_repair else \"n\",\n", " str(res.n_output_tokens), f\"{toks:.1f}\", res.raw_text.replace(\"\\n\", \" \")[:48]))\n", " except Exception as e:\n", " rows.append((mkey, cat, \"json_mode\", \"ERR\", \"\", \"\", \"\", \"\", f\"{type(e).__name__}:{str(e)[:30]}\"))\n", " finally:\n", " runner.close()\n", " if CLEAR_CACHE_AFTER: _free_disk(spec.repo_id)\n", "\n", "hdr = [\"model\", \"category\", \"mode\", \"valid\", \"robust\", \"repair\", \"out_tok\", \"tok/s\", \"raw[:48]\"]\n", "w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", "def line(r): return \" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr)))\n", "print(\"\\n================ BIGGER-BRAINS PROBE (json_mode native) ================\\n\")\n", "print(line(hdr)); print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows: print(line(r))\n", "print(\"\\n---- native bucket (clean valid JSON on its own, across all 3 categories) ----\")\n", "for mkey in MODELS:\n", " jm = [r for r in rows if r[0] == mkey and r[3] in (\"Y\", \"n\")]\n", " if not jm:\n", " print(f\" {mkey:18s} (no rows / load failed)\"); continue\n", " v = sum(1 for r in jm if r[3] == \"Y\"); rob = sum(1 for r in jm if r[4] == \"Y\")\n", " bucket = \"native_capable\" if rob == len(jm) else (\"finetune_candidate\" if v else \"needs-look\")\n", " print(f\" {mkey:18s} valid {v}/{len(jm)} robust {rob}/{len(jm)} -> {bucket}\")\n", "print(\"\\nPaste this whole BIGGER-BRAINS block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 6 — REAL GROUND TRUTH: COCO detection + coordinate-scale CALIBRATION.\n", "# Before trusting any IoU, we must know what coordinate scale the model emits. This cell\n", "# runs qwen3-vl-8b (the native grounding champion) on real COCO val images and scores\n", "# detection under ALL 3 coord spaces x BOTH box formats — the combo with the best F1\n", "# reveals the model's true convention. It also prints raw model output + GT so we can see.\n", "#\n", "# Run on a fresh-ish kernel OR after CELL 4/5 (needs: datasets, the harness, a GPU).\n", "# ==========================================================================================\n", "import warnings, itertools\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from datasets import load_dataset\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task, model_for\n", "from qwen_test_runner.vision.metrics import score_detection\n", "from qwen_test_runner.vision.coords import CoordSpace, XYWH, XYXY\n", "from qwen_test_runner.evaluator import parse_against\n", "\n", "N = 10\n", "MODEL = \"qwen3vl-8b\"\n", "\n", "# ---- load N COCO val samples (streaming) and SHOW the schema so we can see GT format ----\n", "def _load_coco(n):\n", " last_err = None\n", " for repo, split in [(\"detection-datasets/coco\", \"val\"), (\"detection-datasets/coco\", \"validation\"),\n", " (\"rafaelpadilla/coco2017\", \"val\")]:\n", " try:\n", " ds = load_dataset(repo, split=split, streaming=True)\n", " rows = list(itertools.islice(ds, n))\n", " print(f\"loaded {len(rows)} from {repo}:{split}\")\n", " return rows, ds\n", " except Exception as e:\n", " last_err = e\n", " print(f\" {repo}:{split} failed -> {type(e).__name__}: {str(e)[:120]}\")\n", " raise last_err\n", "\n", "rows, ds = _load_coco(N)\n", "print(\"\\n=== dataset features ===\")\n", "try:\n", " print(ds.features)\n", "except Exception as e:\n", " print(\"features unavailable:\", e)\n", "print(\"\\n=== first sample objects (raw) ===\")\n", "print({k: (v if k != \"image\" else f\"\") for k, v in rows[0].items()})\n", "\n", "# ---- category id -> name map (best effort) ----\n", "cat_names = None\n", "try:\n", " obj_feat = ds.features[\"objects\"]\n", " inner = obj_feat.feature if hasattr(obj_feat, \"feature\") else obj_feat\n", " cat_feat = inner[\"category\"] if isinstance(inner, dict) else None\n", " cat_names = getattr(getattr(cat_feat, \"feature\", cat_feat), \"names\", None)\n", "except Exception as e:\n", " print(\"cat-name lookup failed:\", e)\n", "def _cat(cid):\n", " if cat_names and isinstance(cid, int) and 0 <= cid < len(cat_names):\n", " return cat_names[cid]\n", " return str(cid)\n", "\n", "# ---- build GT samples from whatever schema we got ----\n", "def _gt_from(sample):\n", " img = sample[\"image\"]\n", " objs = sample.get(\"objects\", {})\n", " bboxes = objs.get(\"bbox\") or objs.get(\"bboxes\") or []\n", " cats = objs.get(\"category\") or objs.get(\"categories\") or objs.get(\"label\") or []\n", " boxes = [{\"label\": str(_cat(c)).lower().replace(\"_\", \" \"), \"bbox\": list(map(float, b))}\n", " for b, c in zip(bboxes, cats)]\n", " return img, boxes, img.size\n", "\n", "samples = [_gt_from(r) for r in rows]\n", "print(f\"\\nfirst GT boxes: {samples[0][1][:3]} | image size {samples[0][2]}\")\n", "\n", "# ---- run the model ----\n", "spec = get_model(MODEL)\n", "task = get_task(\"bbox_grounding\")\n", "runner = VLMRunner(model_id=spec.repo_id, loader_kind=spec.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", "preds = []\n", "for i, (img, boxes, size) in enumerate(samples):\n", " res = runner.generate(task, img, \"json_mode\", image_id=f\"coco{i}\", image_size=size)\n", " preds.append(res.raw_text)\n", " if i < 2:\n", " print(f\"\\n--- sample {i} (size {size}) RAW MODEL OUTPUT ---\\n{res.raw_text[:400]}\")\n", " print(f\"--- sample {i} GT boxes (pixel) ---\\n{boxes[:4]}\")\n", "runner.close()\n", "\n", "# ---- CALIBRATE: score detection under every coord_space x box-fmt; best F1 = the convention ----\n", "Model = model_for(task)\n", "print(\"\\n================ COORDINATE CALIBRATION (mean detection F1) ================\")\n", "print(f\"{'coord_space':14s} {'gt_fmt':6s} meanF1 (over {N} images)\")\n", "best = (None, None, -1.0)\n", "for space in [CoordSpace.NORM_0_1000, CoordSpace.NORM_0_1, CoordSpace.PIXEL_ABS]:\n", " for fmt in [XYWH, XYXY]:\n", " f1s = []\n", " for (img, boxes, size), raw in zip(samples, preds):\n", " pr = parse_against(raw, Model)\n", " if not pr.schema_valid:\n", " f1s.append(0.0); continue\n", " gt = {\"boxes\": [{\"label\": b[\"label\"], \"bbox\": b[\"bbox\"], \"fmt\": fmt} for b in boxes]}\n", " ctx = {\"size\": size, \"coord_space\": space}\n", " try:\n", " primary, _ = score_detection(pr.parsed.model_dump(), gt, ctx)\n", " f1s.append(primary or 0.0)\n", " except Exception:\n", " f1s.append(0.0)\n", " mean = sum(f1s) / len(f1s) if f1s else 0.0\n", " print(f\"{space.value:14s} {fmt:6s} {mean:.3f}\")\n", " if mean > best[2]:\n", " best = (space.value, fmt, mean)\n", "print(f\"\\nBEST: coord_space={best[0]} gt_fmt={best[1]} meanF1={best[2]:.3f}\")\n", "print(\"(Low across the board can mean labels mismatch COCO names, not coords — check the RAW vs GT above.)\")\n", "print(\"\\nPaste this whole CELL-6 block back (especially the RAW outputs, GT boxes, and the calibration table).\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 7 — REAL DETECTION ACCURACY (v3): native models on real COCO, with the upgraded\n", "# scorer (tolerant labels + class-agnostic localization).\n", "#\n", "# REQUIRES THE v3 ZIP. Do: Runtime -> Restart runtime, drag qwen_vlmbench_colab_v3.zip\n", "# into /content, re-run CELL 1 (it re-extracts + reinstalls), THEN run this cell.\n", "# (Restart matters so the new package + xgrammar-before-import both take effect.)\n", "#\n", "# Reports per model: labeled F1 (tv==television etc.), class-agnostic LOCALIZATION F1\n", "# (can it box objects at all), recall, count error, tok/s. Clears HF cache between models.\n", "# ==========================================================================================\n", "import os, shutil, warnings, subprocess\n", "warnings.filterwarnings(\"ignore\")\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "# xgrammar BEFORE importing the harness so v3's lazy detection turns constrained ON\n", "subprocess.run(\"pip -q install xgrammar\", shell=True)\n", "try:\n", " import xgrammar; print(\"xgrammar:\", getattr(xgrammar, \"__version__\", \"?\"))\n", "except Exception as e:\n", " print(\"xgrammar not available:\", e)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from qwen_test_runner.vision.datasets import load_coco_detection\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"] # native champions; edit freely\n", "N = 20\n", "MODES = [\"json_mode\"] # add \"constrained\" to test the grammar path (v3 lazy-xgrammar)\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "print(f\"\\nloading {N} COCO val images …\")\n", "samples = load_coco_detection(n=N, split=\"val\")\n", "print(f\"got {len(samples)} samples; first GT: {samples[0].gt['boxes'][:2]} size={samples[0].size}\")\n", "\n", "spec = get_task(\"bbox_grounding\")\n", "summary = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} ({ms.repo_id}) #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\",\n", " device_map=\"cuda\")\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None)\n", " try:\n", " for mode in MODES:\n", " results = []\n", " for s in samples:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " results.append((mr, res))\n", " if s is samples[0]:\n", " print(f\" [{mode}] sample0 raw: {res.raw_text[:160]}\")\n", " mrs = [r[0] for r in results]\n", " run = score_vision_run(mrs, model=mkey, category=\"bbox_grounding\", mode=mode)\n", " loc = sum(r.metrics.get(\"localization_f1\", 0.0) for r in mrs if r.schema_valid)\n", " rec = sum(r.metrics.get(\"recall\", 0.0) for r in mrs if r.schema_valid)\n", " cerr = sum(r.metrics.get(\"count_abs_err\", 0.0) for r in mrs if r.schema_valid)\n", " nv = sum(1 for r in mrs if r.schema_valid) or 1\n", " summary.append({\n", " \"model\": mkey, \"mode\": mode, \"backend\": results[0][1].backend,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"labeledF1\": run.primary_score_mean or 0.0,\n", " \"localF1\": loc / nv, \"recall\": rec / nv, \"count_err\": cerr / nv,\n", " \"tok_s\": run.tokens_per_sec,\n", " })\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "print(\"\\n================ REAL COCO DETECTION (N=%d) ================\\n\" % N)\n", "hdr = [\"model\", \"mode\", \"backend\", \"valid\", \"robust\", \"labeledF1\", \"localF1\", \"recall\", \"count_err\", \"tok/s\"]\n", "def fmt(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(fmt(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + summary)) for h in hdr]\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in summary:\n", " print(\" \".join(fmt(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nlabeledF1 = boxes matched WITH a tolerant label (tv==television); \"\n", " \"localF1 = boxes matched ignoring labels (pure localization).\")\n", "print(\"Paste this whole CELL-7 block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 8 — DECISIVE: json_mode vs CONSTRAINED on real COCO (recovers the lost validity).\n", "# Self-contained; runs in the current v3 kernel (xgrammar already active). No re-drag.\n", "# Fixes the two validity killers from CELL 7: (1) bumps max_new_tokens 768->2048 to stop\n", "# truncation, (2) tightens the prompt against bare top-level arrays. Then compares\n", "# json_mode (prompt-only) vs constrained (grammar-FORCED object) — the grammar makes a\n", "# bare array / fence / truncated-invalid impossible, so validity should jump.\n", "# ==========================================================================================\n", "import os, shutil, warnings, dataclasses\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "\n", "from qwen_test_runner.vision.datasets import load_coco_detection\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "from qwen_test_runner.vision.coords import prompt_hint_for\n", "import qwen_test_runner.vision.runners as _R\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "N = 20\n", "MODES = [\"json_mode\", \"constrained\"]\n", "\n", "# stronger bbox prompt (single OBJECT, not a bare array; bounded count). Patch the runner's\n", "# resolver so it applies in-place without a re-drag; non-bbox categories keep their prompt.\n", "_BBOX_PROMPT = (\n", " \"You are an object detector. Find the distinct objects in the image. Output ONLY a single \"\n", " \"raw JSON OBJECT — NOT a bare array, NO markdown code fences. It must match exactly:\\n\"\n", " '{\"detections\": [{\"label\": \"\", \"box\": [x1, y1, x2, y2], \"score\": }], '\n", " '\"count\": }\\nReport the most prominent objects, up to 30. {coord_hint} '\n", " 'Use the key \"box\" (NOT bbox_2d) with four numbers [x1, y1, x2, y2].'\n", ")\n", "def _resolved(spec):\n", " if spec.category == \"bbox_grounding\":\n", " return _BBOX_PROMPT.replace(\"{coord_hint}\", prompt_hint_for(spec.coord_space))\n", " return spec.system_prompt\n", "_R.resolved_system_prompt = _resolved\n", "\n", "spec = dataclasses.replace(get_task(\"bbox_grounding\"), max_new_tokens=2048)\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "print(f\"loading {N} COCO val images …\")\n", "samples = load_coco_detection(n=N, split=\"val\")\n", "\n", "summary = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " print(\" xgr_compiler active:\", runner._xgr_compiler is not None)\n", " try:\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " try:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " except Exception as e:\n", " print(f\" {mode} {s.image_id}: ERR {type(e).__name__}: {str(e)[:80]}\"); continue\n", " mrs.append(mr)\n", " if s is samples[0]:\n", " print(f\" [{mode}] backend={res.backend} sample0 raw: {res.raw_text[:140]}\")\n", " if not mrs:\n", " continue\n", " run = score_vision_run(mrs, model=mkey, category=\"bbox_grounding\", mode=mode)\n", " nv = sum(1 for r in mrs if r.schema_valid) or 1\n", " summary.append({\n", " \"model\": mkey, \"mode\": mode, \"backend\": mrs and \"mix\",\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"labeledF1\": run.primary_score_mean or 0.0,\n", " \"localF1\": sum(r.metrics.get(\"localization_f1\", 0.0) for r in mrs if r.schema_valid) / nv,\n", " \"recall\": sum(r.metrics.get(\"recall\", 0.0) for r in mrs if r.schema_valid) / nv,\n", " \"tok/s\": run.tokens_per_sec,\n", " })\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"mode\", \"valid\", \"robust\", \"labeledF1\", \"localF1\", \"recall\", \"tok/s\"]\n", "def _fmt(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(_fmt(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + summary)) for h in hdr]\n", "print(\"\\n================ json_mode vs CONSTRAINED on real COCO (N=%d) ================\\n\" % N)\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in summary:\n", " print(\" \".join(_fmt(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nIf constrained lifts 'valid' toward 20/20 while json_mode stays ~half, the grammar fallback works:\")\n", "print(\"a capable model + grammar = robust labeler. Paste this whole CELL-8 block back.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 9 — the other two PILOTS: OCR (TextVQA) + classification (food101) accuracy.\n", "# Self-contained, current v3 kernel, no re-drag. Native champions, json_mode + constrained.\n", "# classification uses the TOLERANT matcher (food101 'spaghetti_bolognese' ~ model 'spaghetti').\n", "# Reports valid / robust / accuracy / EFFECTIVE (acc x valid = real yield) / tok/s.\n", "# ==========================================================================================\n", "import os, shutil, warnings, itertools, dataclasses\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True)\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "from datasets import load_dataset\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task, model_for, resolved_system_prompt\n", "from qwen_test_runner.vision.metrics import score_vision_sample, labels_match, _norm_text\n", "from qwen_test_runner.evaluator import parse_against\n", "import qwen_test_runner.vision.runners as _R\n", "_R.resolved_system_prompt = resolved_system_prompt # undo any earlier monkeypatch\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "N = 20\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "# ---- load GT (defensive: try ids, print structure once) ----\n", "def _try_load(repos, split, n):\n", " for repo in repos:\n", " try:\n", " ds = load_dataset(repo, split=split, streaming=True)\n", " rows = list(itertools.islice(ds, n))\n", " print(f\"loaded {len(rows)} from {repo}:{split}\")\n", " return rows, ds\n", " except Exception as e:\n", " print(f\" {repo}:{split} -> {type(e).__name__}: {str(e)[:100]}\")\n", " return [], None\n", "\n", "ocr_rows, _ = _try_load([\"textvqa\", \"lmms-lab/textvqa\", \"facebook/textvqa\"], \"validation\", N)\n", "cls_rows, cls_ds = _try_load([\"food101\", \"ethz/food101\"], \"validation\", N)\n", "cls_names = None\n", "try:\n", " cls_names = cls_ds.features[\"label\"].names\n", "except Exception:\n", " pass\n", "def _cls_name(lbl):\n", " if cls_names and isinstance(lbl, int) and 0 <= lbl < len(cls_names):\n", " return cls_names[lbl].replace(\"_\", \" \")\n", " return str(lbl).replace(\"_\", \" \")\n", "\n", "ocr_samples = [(r[\"image\"], (r.get(\"answers\") or [r.get(\"answer\", \"\")]))\n", " for r in ocr_rows if r.get(\"image\") is not None]\n", "cls_samples = [(r[\"image\"], _cls_name(r.get(\"label\"))) for r in cls_rows if r.get(\"image\") is not None]\n", "if ocr_samples:\n", " print(\"OCR sample0 answers:\", ocr_samples[0][1][:5])\n", "if cls_samples:\n", " print(\"CLS sample0 label:\", cls_samples[0][1])\n", "\n", "ocr_spec = get_task(\"ocr_text\")\n", "cls_spec = get_task(\"image_classification\")\n", "ClsModel = model_for(cls_spec)\n", "rows_out = []\n", "\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " try:\n", " for mode in MODES:\n", " # ---- OCR ----\n", " mrs = []\n", " for img, answers in ocr_samples:\n", " res = runner.generate(ocr_spec, img, mode, image_id=\"o\", image_size=img.size)\n", " # credit if ANY gold answer is read\n", " best = 0.0\n", " for a in answers[:5]:\n", " mr = score_vision_sample(ocr_spec, res.raw_text, {\"text\": a}, mode=mode,\n", " image_id=\"o\", image_size=img.size,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " if mr.schema_valid and (mr.primary_score or 0) > best:\n", " best = mr.primary_score\n", " mrs.append((mr.schema_valid, mr.json_robust, best if mr.schema_valid else 0.0,\n", " res.n_output_tokens, res.gen_seconds))\n", " if mrs:\n", " nv = sum(1 for v, *_ in mrs if v) or 1\n", " acc = sum(s for v, _, s, *_ in mrs if v) / nv\n", " rows_out.append({\"model\": mkey, \"task\": \"ocr\", \"mode\": mode,\n", " \"valid\": f\"{sum(1 for v,*_ in mrs if v)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for _,r,*_ in mrs if r)}/{len(mrs)}\",\n", " \"acc\": acc, \"effective\": acc * (sum(1 for v,*_ in mrs if v) / len(mrs)),\n", " \"tok/s\": sum(t for *_, t, _ in mrs) / max(1e-9, sum(g for *_, g in mrs))})\n", " # ---- classification ----\n", " crs = []\n", " for img, name in cls_samples:\n", " res = runner.generate(cls_spec, img, mode, image_id=\"c\", image_size=img.size)\n", " pr = parse_against(res.raw_text, ClsModel)\n", " ok = pr.schema_valid\n", " top1 = 1.0 if (ok and labels_match(str(pr.parsed.label), name)) else 0.0\n", " crs.append((ok, ok and not pr.needed_structural_repair, top1,\n", " res.n_output_tokens, res.gen_seconds))\n", " if crs:\n", " nv = sum(1 for v, *_ in crs if v) or 1\n", " acc = sum(s for v, _, s, *_ in crs if v) / nv\n", " rows_out.append({\"model\": mkey, \"task\": \"cls\", \"mode\": mode,\n", " \"valid\": f\"{sum(1 for v,*_ in crs if v)}/{len(crs)}\",\n", " \"robust\": f\"{sum(1 for _,r,*_ in crs if r)}/{len(crs)}\",\n", " \"acc\": acc, \"effective\": acc * (sum(1 for v,*_ in crs if v) / len(crs)),\n", " \"tok/s\": sum(t for *_, t, _ in crs) / max(1e-9, sum(g for *_, g in crs))})\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"task\", \"mode\", \"valid\", \"robust\", \"acc\", \"effective\", \"tok/s\"]\n", "def _f(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(_f(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + rows_out)) for h in hdr]\n", "print(\"\\n================ OCR (TextVQA) + CLASSIFICATION (food101) ================\\n\")\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows_out:\n", " print(\" \".join(_f(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nPaste this whole CELL-9 block back. 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"0097842d7102461cb75651b9dbe6f983", "db2eef5b99164cd3b65d9eed78d18987", "0fa536c0028b4e31bd520898a7f36eb3", "a9524d3024774be78f7928e0753588a2", "ff2b84a7998b4e41afe2f3896dc92e60", "243fb05a74f644c9ae638432ca209c93", "6279f4280cd74c8abac2f6931d1bf42b", "1ef3ee6a2f0945378b1c738f246b288a", "02a89f82e0294853aa7e6ad85cbb65c9", "e14ef87acdfa4291b8078a34999080c0" ] }, "id": "yFKhcoJNDDFR", "outputId": "77ca0c8e-5f7e-4a83-8511-99ffb23f1142" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: /content/qwen_vlmbench_colab_v3.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260628T022614_167154Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "loading AbstractPhil/Qwen3.5-0.8B-json-captioner | est VRAM bf16: 1.746 GB\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00\n", "\n", "\n", "\n", "{\"label\": \"blue color\", \"confidence\": 1.0}\n", "user\n", "What is the most prominent object or scene category in the image?\n", "Answer: blue color\n", "assistant\n", "\n", "\n", "\n", "{\"label\": \"blue color\", \"confidence\": 1.0}\n", "\n", "\n", "schema_valid: True | needed_repair: True | json_robust: False | out_tokens: 109 | gen_seconds: 1.29\n", "REAL VLMRunner PATH WORKS.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed — fast path stays off'\n", "\n", "$ pip -q install flash-linear-attention || echo 'fla install failed — ok'\n", "\n", "$ pip -q install xgrammar || echo 'xgrammar build failed — constrained mode will fall back to json_mode'\n", "\n", "##### loading qwen3.5-0.8b-json-captioner (AbstractPhil/Qwen3.5-0.8B-json-captioner, est 1.7GB bf16) #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate\n", " qwen3.5-0.8b valid 2/3 robust 2/3 -> finetune_candidate\n", " qwen3.5-2b valid 2/3 robust 2/3 -> finetune_candidate\n", " qwen3.5-4b valid 2/3 robust 2/3 -> finetune_candidate\n", "\n", "Paste this whole PROBE block back.\n", "\n", "$ pip -q install 'git+https://github.com/huggingface/transformers.git@main' qwen-vl-utils accelerate pillow\n", "\n", "$ pip -q install causal-conv1d || echo 'causal-conv1d build failed'\n", "\n", "=== installing xgrammar (FULL output so we can see any error) ===\n", "\n", "xgrammar import OK — version ?\n", "prompts patched (anti-fence + explicit schema). constrained backend will be: xgrammar\n", "\n", "##### loading qwen3.5-0.8b-json-captioner #####\n", "[VLMRunner] loading AbstractPhil/Qwen3.5-0.8B-json-captioner (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/473 [00:00 finetune_candidate | constrained valid 2/3\n", " qwen3.5-0.8b json_mode valid 1/3 robust 1/3 -> finetune_candidate | constrained valid 3/3\n", " qwen3.5-2b json_mode valid 2/3 robust 2/3 -> finetune_candidate | constrained valid 3/3\n", " qwen3.5-4b json_mode valid 3/3 robust 3/3 -> native_capable | constrained valid 3/3\n", "\n", "Paste this whole PROBE v2 block back (include the 'xgr_compiler active' lines).\n", "\n", "##### loading qwen3.5-9b (Qwen/Qwen3.5-9B, ~18GB, MoE=False) #####\n", "[VLMRunner] loading Qwen/Qwen3.5-9B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00 native_capable\n", " qwen3.5-27b valid 3/3 robust 3/3 -> native_capable\n", " qwen3.5-35b-a3b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-8b valid 3/3 robust 3/3 -> native_capable\n", " qwen3vl-32b valid 3/3 robust 3/3 -> native_capable\n", "\n", "Paste this whole BIGGER-BRAINS block back.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/40 [00:00', 'width': 640, 'height': 426, 'objects': {'bbox_id': [26547, 34646, 35802, 103487, 104368, 105328, 110334, 230831, 233201, 1640282, 1647285, 1648594, 1654394, 1666628, 1667817, 1669970, 1941808, 2146194, 2146548, 2204286], 'category': [58, 62, 62, 56, 56, 56, 56, 0, 0, 68, 72, 73, 73, 74, 75, 75, 56, 75, 75, 60], 'bbox': [[236.98, 142.51, 261.68, 212.01], [7.03, 167.76, 156.35, 262.63], [557.21, 209.19, 638.5600000000001, 287.92], [358.98, 218.05, 414.98, 320.88], [290.69, 218.0, 352.52, 316.48], [413.2, 223.01, 443.37, 304.37], [317.4, 219.24, 338.97999999999996, 230.83], [412.8, 157.61, 465.85, 295.62], [384.43, 172.21, 399.55, 207.95000000000002], [512.22, 205.75, 526.96, 221.72], [493.1, 174.34, 513.39, 282.65], [604.77, 305.89, 619.11, 351.59999999999997], [613.24, 308.24, 626.12, 354.68], [447.77, 121.12, 461.74, 143.0], [549.06, 309.43, 585.7399999999999, 399.1], [350.76, 208.84, 362.13, 231.39000000000001], [412.25, 219.02, 421.88, 231.54000000000002], [241.24, 194.99, 255.46, 212.62], [336.79, 199.5, 346.52000000000004, 216.23], [321.21, 231.22, 446.77, 320.15]], 'area': [531.8071000000001, 13244.657700000002, 5833.117949999999, 2245.34355, 1833.7840000000017, 1289.3734500000014, 210.14820000000023, 2913.1103999999987, 435.1449499999997, 217.71919999999997, 2089.9747999999986, 338.60884999999973, 322.5935999999982, 225.6642000000005, 2171.6188500000007, 178.18510000000012, 90.98724999999988, 189.56010000000012, 120.23200000000004, 2362.4897499999984]}}\n", "\n", "first GT boxes: [{'label': 'potted plant', 'bbox': [236.98, 142.51, 261.68, 212.01]}, {'label': 'tv', 'bbox': [7.03, 167.76, 156.35, 262.63]}, {'label': 'tv', 'bbox': [557.21, 209.19, 638.5600000000001, 287.92]}] | image size (640, 426)\n", "[VLMRunner] loading Qwen/Qwen3-VL-8B-Instruct (image_text_to_text, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00 RuntimeError: Dataset scripts are no longer supported, but found textvqa.py\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/2.00k [00:00 HfUriError: Invalid HF URI 'hf://datasets/food101@83488de741c1bd1ce27aa6a2b33e19c7bdf92ca9/.huggingface.yaml'. R\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/16.4k [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "# %% =======================================================================================\n", "# CELL 10 — NEW CATEGORIES (v4): data-format recognition + parsing (SELF-CONTAINED).\n", "# REQUIRES v4. Do: Runtime -> Restart runtime, drag qwen_vlmbench_colab_v4.zip into /content,\n", "# re-run CELL 1, then run this. No external dataset — it renders json/yaml/md/csv/xml/toml\n", "# snippets to images with exact GT. Two categories:\n", "# data_type_differentiation -> identify the format (metric: type accuracy)\n", "# data_type_utilization -> re-serialize it to JSON (metric: key/value F1)\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "from qwen_test_runner.vision.datasets import make_datatype_samples\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "N = 18\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "samples = make_datatype_samples(N) # self-contained, no download\n", "print(f\"rendered {len(samples)} data-format images; GT types: \"\n", " f\"{sorted(set(s.gt['data_type'] for s in samples))}\")\n", "specs = {\"diff\": get_task(\"data_type_differentiation\"), \"util\": get_task(\"data_type_utilization\")}\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " try:\n", " for tname, spec in specs.items():\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " mrs.append(mr)\n", " if s is samples[0] and tname == \"util\":\n", " print(f\" [{tname}/{mode}] sample0 ({s.gt['data_type']}) raw: {res.raw_text[:120]}\")\n", " run = score_vision_run(mrs, model=mkey, category=spec.category, mode=mode)\n", " vr = sum(1 for r in mrs if r.schema_valid) / len(mrs)\n", " rows.append({\"model\": mkey, \"task\": tname, \"mode\": mode,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"robust\": f\"{sum(1 for r in mrs if r.json_robust)}/{len(mrs)}\",\n", " \"acc\": run.primary_score_mean or 0.0,\n", " \"effective\": (run.primary_score_mean or 0.0) * vr,\n", " \"tok/s\": run.tokens_per_sec})\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"task\", \"mode\", \"valid\", \"robust\", \"acc\", \"effective\", \"tok/s\"]\n", "def _f(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(_f(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + rows)) for h in hdr]\n", "print(\"\\n================ DATA-FORMAT recognition (diff) + parsing (util) ================\\n\")\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(\" \".join(_f(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\ndiff acc = format identified correctly; util acc = key/value F1 of the re-serialized JSON.\")\n", "print(\"Paste this whole CELL-10 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "8a1a670308db40088a402b664a59dfdb", "3c35c869cf1742758584d8cdbfac4b49", "dbff033311a34b778909f6f9b42d46a2", "f727c932b30c4aef86f3c40d4ef1d70e", "03133c2f6eac433597769c086cd9f30b", "9e47ed377bfb48959da1861cb4066b6a", 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"ab37807defa546069f1259b8d073b105", "4b1552657b4e4961b42d3078dd39b2bf", "9398ce5e0c1c4826ad9c715db2a3f920", "d7dcca8c7eeb486bafc281681fa9284f", "0a83189405654032b80221fe7b6d8be2", "c3b4c8cc403f4e499a506d1b062b53d2", "3fe612b0a1bb466eadacbeee46d2a2c6", "e1f3efce7dd34424b4bedd6d29b6ae82", "eb714a7e73fe4b339a1311438479960a", "701f77ae75604a748ce1d665309e50c9", "7a8509d7a6ec40f6803450a20c766276", "17aa2a4e138042638e56609aab15c6b2" ] }, "id": "ciKUDdRGhg5m", "outputId": "7a885e0f-76eb-45cb-b212-d026b16cd266" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab_v4.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260628T044019_923451Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "rendered 18 data-format images; GT types: ['csv', 'json', 'markdown', 'toml', 'xml', 'yaml']\n", "\n", "##### qwen3.5-2b #####\n", "[VLMRunner] loading Qwen/Qwen3.5-2B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 11 — NEW CATEGORIES (v5): spatial relations + depth ordering + subject fixation.\n", "# REQUIRES v5. Restart -> drag qwen_vlmbench_colab_v5.zip -> CELL 1 -> this cell.\n", "# SELF-CONTAINED: renders colored-circle scenes with exact GT (no download).\n", "# structural_spatial_awareness -> who is left_of whom (triple-F1)\n", "# depth_analysis -> bigger = nearer (order accuracy)\n", "# subject_fixation -> largest shape + box (IoU + label)\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "from qwen_test_runner.vision.datasets import make_shapes_samples\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "N = 12\n", "CATS = [\"structural_spatial_awareness\", \"depth_analysis\", \"subject_fixation\"]\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "samples = make_shapes_samples(N) # one scene set, GT for all three categories\n", "print(f\"rendered {len(samples)} shape scenes (size {samples[0].size})\")\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " try:\n", " for cat in CATS:\n", " spec = get_task(cat)\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id, image_size=s.size)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " mrs.append(mr)\n", " if s is samples[0] and mode == \"constrained\":\n", " print(f\" [{cat[:12]}/{mode}] sample0 raw: {res.raw_text[:120]}\")\n", " run = score_vision_run(mrs, model=mkey, category=cat, mode=mode)\n", " vr = sum(1 for r in mrs if r.schema_valid) / len(mrs)\n", " rows.append({\"model\": mkey, \"cat\": cat.split(\"_\")[0][:8], \"mode\": mode,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"acc\": run.primary_score_mean or 0.0,\n", " \"effective\": (run.primary_score_mean or 0.0) * vr,\n", " \"tok/s\": run.tokens_per_sec})\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"cat\", \"mode\", \"valid\", \"acc\", \"effective\", \"tok/s\"]\n", "def _f(v): return f\"{v:.3f}\" if isinstance(v, float) else str(v)\n", "w = [max(len(_f(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + rows)) for h in hdr]\n", "print(\"\\n================ SPATIAL + DEPTH + SUBJECT-FIXATION (synthetic) ================\\n\")\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(\" \".join(_f(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nspatial=triple-F1, depth=order-acc, subject=IoU+label. Paste this whole CELL-11 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "91cbe6bd5b19485284f6da86f84fe163", "623afeff209045ccb666aa746cd77fd4", "e2b844b585b64472b39655e9046c0fa8", "a18f30e29c6f44fea0cbbcdb2baaa636", "2585827ea6ac47e29ade8b91abc46d09", "0ff423900f324ce4900f2695eb4bfa5c", "0eeadda042aa463f9acd009c133aa4f7", "815a177345f14233afa3835877d5ea8e", "3e8b469411324c8ba3d83cf063e45226", "078b91ea6e9644b7af834633c7ba0399", "e7ae3e92cae346aca5ffc42f0bbe65de", "b32fd8fc341c466ab67944dc6f727aca", "ceba4a5858c74c189ffbc4310586961c", "cd90ea52086349dfb7891636f547d5db", "8554bee52e0d404f904389ea4a2523c6", "f0c0194bbd9748d8b482b9490f39a21f", "bdea0e7409d54a77a7a4e971e0b71248", "855bb1a68fd24297a07c9f1bfc7b1d91", "2c603972d7024fbb838ab385fd902ce0", "b4803e76f77b485d9ef77b5bcb2c7057", "2d878344ce1f4a558c5aa0653511f0cd", "f5f5e12393494557bb3f3cf59b459ca3", "851436f76a0f4d9381e907a32fe1e9f8", 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"68a5a885bba64fdd8cb669c242cc708f", "b47d7899f6004d4cab987a17462b29e5", "5fd486c47131415d92f87b16bf135a66", "cc1de767691c4e548b2cc7b6de74f2f1", "c79e584ec62b489fa4b33d7b6e4f6d01", "c40d575fda7a40d589507f138a118661", "bfec459c37df496d950adf27c97104f0", "b99d458d994a43f9952ee95e6d8f9089", "9236b33eaaa34f1398168e5c8d857275", "887e94a770e94357ab842be0ad7e46fb", "9e295b97954544c894ec5be980d6f970", "f0684decfcef4ed2b3d6651fab769f21", "90d833a21b374b31a8090ad2c027a503", "7c01873183164d559539433761496aee", "c872e46be5d440d3beee693c013bd606", "2de43d3ed9aa4a23b21e83938afd9d4a", "827ad7c552104c69962c973711466f60", "dc9e47fe9a5e44dab3e729af8d629d0b", "0692c9894c1c491fa7ca39a17ad25259", "265dd3b1cc3a41f19b4d09aaca987be9", "6aa7d699eb504e2caf7516c660ede3d6", "7306f5b62dd046ac8bbb753c469ed0c4", "4ad86e233e3b4eb68dbb5664c6758eee", "e1e3f50e3af94c308070b64fce38c453", "3ef1e254dc5a4663882dd88934f06c8c", "b806f0294a334c2aa7e2688aca612cc7", "7478318f479d4a1e8545d0035766ffbd", "710f7ed9fdb143a3bf0feed970f18aad", "b0320b30b2594cf7a9af77f09268b9d2", "a348a41e1e734e1cb72fd919417f73cf", "222d2856c0b44448b0607ac13aaccd71", "a2cce6c0a53d4bab9291ec6242f83fc1", "7246b4a3ffe5429e9ded9288640847de", "6deb0e1e38f84441bc6f4883e1c82da3", "39d699f3b4ab4fdcb66f003981c8285a", "d90caf722bbe454bb7eb1a667ef58783", "4ccf9cf9e18c4e27b4508fb1efc7b77a", "c0d55b67fb354e3d9f930f6cdb60dd59", "b0e96418905a4ae6a480974d69b057dc", "01b402af63f14d8783a7e1ff1a0ec604", "c710b96bf20048a79fbb75bd660a750d", "610d7c7e7c5248ffa37033daf86682da", "35bfb89111e047ba81740405213c4a21", "1e7486cc4b334836a2acfff97c26c24c", "1f821c68937c40578a7ac1e1956078da", "23a93533789b4274885d032ced76b1c4", "11b75cb371004ff4b6d565de713eb32e", "c85585c3df974a669adea0a96627a78c", "3ddd08951720400492fd6b210fabd8fc" ] }, "id": "YP00cimV3CDg", "outputId": "7fe36da7-a584-495f-fdbb-8615657516a1" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab_v5.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260628T061346_065009Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "rendered 12 shape scenes (size (540, 320))\n", "\n", "##### qwen3.5-2b #####\n", "[VLMRunner] loading Qwen/Qwen3.5-2B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 12 — the FINAL 7 categories (v6): segmentation, outline, 3D, camera, semantic, style\n", "# (all SELF-CONTAINED synthetic) + VQA (real GQA, best-effort). All 15 now real.\n", "# REQUIRES v6. Restart -> drag qwen_vlmbench_colab_v6.zip -> CELL 1 -> this cell.\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True) # only needed for the VQA loader\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "from qwen_test_runner.vision import datasets as DS\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "N = 12\n", "\n", "# (category, loader-callable, needs_per_sample_prompt). VQA is real GQA (best-effort).\n", "CATS = [\n", " (\"segmentation\", DS.make_segmentation_samples, False),\n", " (\"outline_association\", DS.make_outline_samples, False),\n", " (\"geometric_3d_object_id\", DS.make_3d_samples, False),\n", " (\"camera_rotational_offset\", DS.make_camera_samples, False),\n", " (\"semantic_association\", DS.make_semantic_samples, False),\n", " (\"style_structural_awareness\", DS.make_style_samples, False),\n", " (\"vit_accuracy_to_prompt\", DS.make_gqa_samples, True),\n", "]\n", "\n", "# pre-load each category's samples ONCE (identical inputs across models)\n", "loaded = []\n", "for cat, loader, needs_prompt in CATS:\n", " try:\n", " samples = loader(n=N)\n", " loaded.append((cat, samples, needs_prompt))\n", " print(f\"loaded {cat}: {len(samples)} samples\")\n", " except Exception as e:\n", " print(f\"SKIP {cat}: loader failed -> {type(e).__name__}: {str(e)[:120]}\")\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " try:\n", " for cat, samples, needs_prompt in loaded:\n", " spec = get_task(cat)\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " up = s.prompt if needs_prompt else None\n", " try:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id,\n", " image_size=s.size, user_prompt=up)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " except Exception as e:\n", " print(f\" {cat}/{mode} ERR {type(e).__name__}: {str(e)[:70]}\"); continue\n", " mrs.append(mr)\n", " if not mrs:\n", " continue\n", " run = score_vision_run(mrs, model=mkey, category=cat, mode=mode)\n", " vr = sum(1 for r in mrs if r.schema_valid) / len(mrs)\n", " rows.append({\"model\": mkey, \"cat\": cat[:14], \"mode\": mode,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"acc\": run.primary_score_mean, \"effective\": (run.primary_score_mean or 0.0) * vr,\n", " \"tok/s\": run.tokens_per_sec})\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"cat\", \"mode\", \"valid\", \"acc\", \"effective\", \"tok/s\"]\n", "def _f(v): return (\"n/a\" if v is None else (f\"{v:.3f}\" if isinstance(v, float) else str(v)))\n", "w = [max(len(_f(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + rows)) for h in hdr]\n", "print(\"\\n================ FINAL 7 CATEGORIES (all 15 now real) ================\\n\")\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(\" \".join(_f(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nseg=mIoU, outline=poly-IoU, 3d=center-match, camera=acc@30deg, semantic=triple-F1, \"\n", " \"style=style-acc, vqa=answer-match. Paste this whole CELL-12 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "6aec35f1973c40c898b09ffc3626979a", "60ccd0b1827f43e9bb493d6402b8188c", "1d2d6d82ec9e459789dc7a8064b04e85", "a2bf40851b1c465f88468de2cb809e91", "29f06380a2f64f31a9b380d631cb2826", "9f702112728840f39e162d9860965b1a", "2bcc45fcad50410aa048426ad2b0bd6f", "7c3831e072a94c7e8be188182cafc920", "92f9df71f3224d679276158829371f15", "d3a7a42453304ced8295fffabc478c1b", "7104442994e74b5d82c7ffd7b4497217", "49bdc28510374367b0ae25ec892dca78", "5fc167897d84415d8dcd33bad47f5852", "4c742a2831514ef5a18aeb1d267d2cd3", "50ba4229098c4d8b8e50e9bfb053bd56", "fa30f83510064b90b8f1065a6f27141b", "6ca098fa371a4a129c5c2a4fee576105", "2b7b6ae649e7479dad8eceb7178f85b3", "f6eb8717110940f89b99de5acd210ea6", "becd34f47c6d417295f552b7a1588fdd", "56d87d56fab44f3ea7ffb1a429503bb7", "efc49eda4c7b43ea8e542478c3a843e7", "fe026f871b2b4b6398e249362166e274", 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"f738724f28d64da99db5469fb3f020f4", "7e903701a9c94aa1bbc2364db648e120", "dda3cfc420c34a0aa38ef265172e1e0b", "9dc6850b390d4dcbb21cf30383831870", "6e213c6610324c5481e933402cf62351", "9007ef61c6414966a172aebcfd595d45", "0b9fc1d0bafe41b0abdadf8825f8d631", "5772b6f885854c708b657c5fcc1d9e86", "55fc843ab1c848cabe02c2d04388413d", "b93ffa4ee5e342e5bba7f501a261e7ae", "649cdb91e0754e82a8df4b5abdcbb46b", "338cf4e6900c4f12995f8ae5a001b945", "ccc19f23aa36435a81592ab3e396874f", "f21c232e99064e01aaac9ff5867d8ab3", "dcb1e96f4cc54ac991b992bbc48577b3", "620bcc6999984b4786041210f53b4403", "28814acae0af4e7d80d7b47d7767beb8", "061ff7696c25478785d5b215bb17663e", "d27c245498eb4a78b23dfd879e3ec719", "cc6f5ad0fd5944749b79738eee266556", "91e31e0385ab421c8fd7931b4a1b9752", "fdbde8ef1eb0424dbcd04b5ca6125e51", "d491ff240fcb43229b804abc2790156b", "18108166e28e45fd8f8670c278f39d29", "f0230bacceff49d0a75190d6fe911588", "b760ab2638144ec8b112ea1f0cec6cdb", "c1ac431d1f95418697a36c317a992afc", "e7112310958f4da485a114699cdf4c1e", "5bc35ee6c01b4c299d6983fc7d257919", "a402e46172674bc8a9590a005547d4cc", "408fc2b6469a49039c1d65666f117e5e", "03944d2e4c53405d968e1baddd2de6c7", "450b56773ccf482097004734a7c07ec7", "2735943fc4904fcdb2cf044c879fe20b", "b6fd197937844a98a7a8eb9028b194ac", "1cee65ceb3b6472f8a97575ab40cc9f2", "eec92d9691e449a49cf1418c62dbdb3b", "78bd4e9db94644ebaaf8539ad18911f1", "d468b3c80c4e4f59b03c8af8e4640fd7", "bed893372ab34dc7a983b200ccdc0827", "6efd75eb9d8e4c0eb89f1d901cbbe491", "4adb55b575c34572a4cf9d8d5ca78faa", "8e6e9fbeb615479daddc00151353c6fb", "69d7d805c1f34e8e99bf70b59422dc84", "3ab984aa714747f692e5abb3f85097c1", "22a7f21dc4994b119d3b3bcdfeb1aa47", "f13dfe5e6c1d417aa3ea23024389af9d", "bbcafbe4b8354268ac2727b2f621f09f", "612cb89c152e4ed09f898a00f2ecd3f7", "aa9a89e04c71489ab9de431ba0deb28d", "c4170a587ad149b4a9f6919960701dd5", "569441ce30bc43668ab4075b212730a0", "bafe122e715e452ea0b065d3581becee", "295f547ddc904997a4a5a266f76acf33", "645e6f26cba143228da289218225970a", "29391b2093f14af9b35f3b23467de9c8", "097a18d167bd498fb28da9e72953dff9", "7f60f00e05724ac8ba72d5465a896bff", "2880b36de61940ac9042ff9698f29ef1", "6b67c94ec8ed45a8a882707318aa1ca7", "ca09ad579e094b028f8beeaeda98d4ac", "07fcd4efc7d248e4baf885ab035fdd7f", "2cd93743b99a44a49e5c33f3cca45119", "ff2daf32ddf04a42a889ddea7c8c6124", "5051dcca70c9488ba3d66a1a44026848", "8e7bd3d3991f49f9be685cf85f8cd949", "2ec8c32c63484832867ef22e0e1888ef", "c3177699e29c4fbfb5d857ee5e10ab7f", "72b21357b86b467eaa462a748475bf47", "833d6bdfa70d401dbd65348a48e4130c", "203fddd863e74577967620043044dd49", "a2b8f19c21e043af9bb412d874033481", "47f1295408c942f68dec51967ac27a0c", "cef0ee387c9146d890f7ce44c047e574", "79c549ecb3574d19acc5b3e33a80b3d9", "aa8d3c305ee14851841e08d173b25c72", "84939b71075d439ab1825abfffb61e0c", "ed7a6ba461a443ffbb0842a9d053dab0", "f309d40647034d22a47365392cc6257e", "6f647b1234e740879c8baff2a9176b9f", "73709028b71b43ea88901e86e6a9d2c6", "41a1e7f431e14702b8408bc6558055c8" ] }, "id": "lHTZC-YILzJL", "outputId": "97270e86-b0bb-4e3f-ceac-78775959e171" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab_v6.zip -> /content/qwen-test-runner\n", "contents: ['.pytest_cache', 'LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'qwen_test_runner.egg-info', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260628T074503_097597Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "loaded segmentation: 12 samples\n", "loaded outline_association: 12 samples\n", "loaded geometric_3d_object_id: 12 samples\n", "loaded camera_rotational_offset: 12 samples\n", "loaded semantic_association: 12 samples\n", "loaded style_structural_awareness: 12 samples\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/15.8k [00:00 RuntimeError: Dataset scripts are no longer supported, but found VQAv2.py\n", "\n", "##### qwen3.5-2b #####\n", "[VLMRunner] loading Qwen/Qwen3.5-2B (multimodal_lm, bf16) on cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "preprocessor_config.json: 0%| | 0.00/390 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "\n", "# %% =======================================================================================\n", "# CELL 12 — the FINAL 7 categories (v6): segmentation, outline, 3D, camera, semantic, style\n", "# (all SELF-CONTAINED synthetic) + VQA (real GQA, best-effort). All 15 now real.\n", "# REQUIRES v6. Restart -> drag qwen_vlmbench_colab_v6.zip -> CELL 1 -> this cell.\n", "# ==========================================================================================\n", "import os, shutil, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import subprocess\n", "subprocess.run(\"pip -q install datasets\", shell=True) # only needed for the VQA loader\n", "try:\n", " import transformers; transformers.logging.set_verbosity_error()\n", "except Exception:\n", " pass\n", "from qwen_test_runner.vision.runners import VLMRunner\n", "from qwen_test_runner.vision.model_registry import get_model\n", "from qwen_test_runner.vision.tasks_vision import get_task\n", "from qwen_test_runner.vision.metrics import score_vision_sample, score_vision_run\n", "from qwen_test_runner.vision import datasets as DS\n", "\n", "MODELS = [\"qwen3.5-2b\", \"qwen3.5-9b\", \"qwen3vl-8b\"]\n", "MODES = [\"json_mode\", \"constrained\"]\n", "N = 48\n", "\n", "# (category, loader-callable, needs_per_sample_prompt). VQA is real GQA (best-effort).\n", "CATS = [\n", " (\"segmentation\", DS.make_segmentation_samples, False),\n", " (\"outline_association\", DS.make_outline_samples, False),\n", " (\"geometric_3d_object_id\", DS.make_3d_samples, False),\n", " (\"camera_rotational_offset\", DS.make_camera_samples, False),\n", " (\"semantic_association\", DS.make_semantic_samples, False),\n", " (\"style_structural_awareness\", DS.make_style_samples, False),\n", " (\"vit_accuracy_to_prompt\", DS.make_gqa_samples, True),\n", "]\n", "\n", "# pre-load each category's samples ONCE (identical inputs across models)\n", "loaded = []\n", "for cat, loader, needs_prompt in CATS:\n", " try:\n", " samples = loader(n=N)\n", " loaded.append((cat, samples, needs_prompt))\n", " print(f\"loaded {cat}: {len(samples)} samples\")\n", " except Exception as e:\n", " print(f\"SKIP {cat}: loader failed -> {type(e).__name__}: {str(e)[:120]}\")\n", "\n", "def _free_disk(repo_id):\n", " p = os.path.expanduser(f\"~/.cache/huggingface/hub/models--{repo_id.replace('/', '--')}\")\n", " if os.path.isdir(p):\n", " shutil.rmtree(p, ignore_errors=True)\n", "\n", "rows = []\n", "for mkey in MODELS:\n", " ms = get_model(mkey)\n", " print(f\"\\n##### {mkey} #####\")\n", " runner = VLMRunner(model_id=ms.repo_id, loader_kind=ms.loader_kind, precision=\"bf16\", device_map=\"cuda\")\n", " try:\n", " for cat, samples, needs_prompt in loaded:\n", " spec = get_task(cat)\n", " for mode in MODES:\n", " mrs = []\n", " for s in samples:\n", " up = s.prompt if needs_prompt else None\n", " try:\n", " res = runner.generate(spec, s.image, mode, image_id=s.image_id,\n", " image_size=s.size, user_prompt=up)\n", " mr = score_vision_sample(spec, res.raw_text, s.gt, mode=mode, image_id=s.image_id,\n", " image_size=s.size, grammar_conformant=res.grammar_conformant,\n", " n_output_tokens=res.n_output_tokens, gen_seconds=res.gen_seconds)\n", " except Exception as e:\n", " print(f\" {cat}/{mode} ERR {type(e).__name__}: {str(e)[:70]}\"); continue\n", " mrs.append(mr)\n", " if not mrs:\n", " continue\n", " run = score_vision_run(mrs, model=mkey, category=cat, mode=mode)\n", " vr = sum(1 for r in mrs if r.schema_valid) / len(mrs)\n", " rows.append({\"model\": mkey, \"cat\": cat[:14], \"mode\": mode,\n", " \"valid\": f\"{sum(1 for r in mrs if r.schema_valid)}/{len(mrs)}\",\n", " \"acc\": run.primary_score_mean, \"effective\": (run.primary_score_mean or 0.0) * vr,\n", " \"tok/s\": run.tokens_per_sec})\n", " finally:\n", " runner.close(); _free_disk(ms.repo_id)\n", "\n", "hdr = [\"model\", \"cat\", \"mode\", \"valid\", \"acc\", \"effective\", \"tok/s\"]\n", "def _f(v): return (\"n/a\" if v is None else (f\"{v:.3f}\" if isinstance(v, float) else str(v)))\n", "w = [max(len(_f(r.get(h, \"\"))) for r in ([dict(zip(hdr, hdr))] + rows)) for h in hdr]\n", "print(\"\\n================ FINAL 7 CATEGORIES (all 15 now real) ================\\n\")\n", "print(\" \".join(h.ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", "for r in rows:\n", " print(\" \".join(_f(r.get(h, \"\")).ljust(w[i]) for i, h in enumerate(hdr)))\n", "print(\"\\nseg=mIoU, outline=poly-IoU, 3d=center-match, camera=acc@30deg, semantic=triple-F1, \"\n", " \"style=style-acc, vqa=answer-match. Paste this whole CELL-12 block back.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "89832c5d979f4fdf9d8c5ee313bf207e", "5129e77b741c433f9c235b14e3fd0772", "e6fbddf2cd774fe9aee1ba629786ec4b", "a5443753742d4646a62f0155c3ed5928", "f24b80540c8741769bee141072212ae9", "5e6d3b9900a4498eb4f346ac0b99a4a5", "265290a6852b477d8df49b678009afff", "3359332376854355948dc8e4bcb6d377", "ba6982b5af0b4f6fa34b2beb27a0aef2", "628e76ef926646b790aa3d82fe4ce4c8", "e868fb32362f4b23acf903656121a8c4", "573ba779fa9e44ef8502fe237cd12f09", "3a85dbc4ba6d403f921d6d97025166fc", "a64cb182f9a74f798ba9df1016ca67c4", "d36e5aee07ad4a8fb3a9ea83ff7c5520", "a5d1ab94ce6c49889046bc0505ff0ec2", "185491cd8d4d4f1daae19a435779e7f5", "8de2fd7caf064c589afd76755492f81f", "86ff4bbc74614053b322a58e32636325", "7d26300de8bc4b4dacf3bbde5c34ec8d", "7240a0a2cd124761a342c10476debbcb", "99bfe8b0ce2e4b2aac375bc8078ab635", "6e328bc096304ebda272b9d7e7b1168c", 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pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260629T162833_635162Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "loaded segmentation: 48 samples\n", "loaded outline_association: 48 samples\n", "loaded geometric_3d_object_id: 48 samples\n", "loaded camera_rotational_offset: 48 samples\n", "loaded semantic_association: 48 samples\n", "loaded style_structural_awareness: 48 samples\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/962 [00:00\", PKG)\n", "print(\"contents:\", sorted(os.listdir(PKG)))\n", "\n", "# 2) minimal deps — the vision STUB path + tests are torch-free (only need pydantic).\n", "# Install the package WITHOUT its heavy core deps (transformers@git etc.); a real\n", "# model run pulls those in CELL 2.\n", "sh(\"pip -q install pydantic pytest\")\n", "sh(f\"pip -q install --no-deps -e {PKG}\")\n", "\n", "# 3) run the torch-free vision test suite (44 tests) IN this environment\n", "os.chdir(PKG)\n", "sh(\"python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py \"\n", " \"tests/test_vision_metrics.py tests/test_vision_bench.py -q\")\n", "\n", "# 4) run the OFFLINE stub benchmark -> writes a real leaderboard with no model download\n", "sh(\"python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke \"\n", " \"--models qwen3.5-9b qwen3.5-0.8b-json-captioner \"\n", " \"--categories image_classification bbox_grounding ocr_text \"\n", " \"--modes json_mode constrained --n 3 --output-root /content/runs_vision\")\n", "\n", "# 5) print the leaderboard + environment readiness\n", "lbs = sorted(glob.glob(\"/content/runs_vision/*/leaderboard.md\"))\n", "if lbs:\n", " print(\"\\n================= LEADERBOARD =================\\n\")\n", " print(open(lbs[-1], encoding=\"utf-8\").read())\n", "sh(\"nvidia-smi -L\")\n", "sh('python -c \"import torch; print(\\'torch\\', torch.__version__, \\'| cuda\\', torch.cuda.is_available(), '\n", " '\\'|\\', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else \\'cpu\\'))\"')\n", "print(\"\\nESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, \"\n", " \"stub benchmark ran and produced a leaderboard on this machine.\")\n", "\n", "# %% =======================================================================================\n", "# CELL 13 — THE FULL ARRAY: every fitting model x all 15 categories x both modes.\n", "# The authoritative benchmark run. Uses the qwen-vlmbench CLI (durable: per-row JSONL +\n", "# resume on disconnect) so a multi-hour run survives. Clears each model's HF cache after\n", "# use so ~320GB of weights don't fill the 230GB SSD (peak ~= one model at a time).\n", "#\n", "# REQUIRES v8. Restart -> drag qwen_vlmbench_colab_v8.zip -> CELL 1 -> this cell.\n", "# Runtime: hours (the 27B/35B/32B are slow). If Colab disconnects, JUST RE-RUN THIS CELL —\n", "# it resumes from where it stopped (skips completed model/category/mode/image keys).\n", "# ==========================================================================================\n", "import os, glob, json, subprocess\n", "\n", "# 1) FULL stack + xgrammar BEFORE the harness imports (so constrained mode is real), + fast path\n", "subprocess.run(\"pip -q install 'git+https://github.com/huggingface/transformers.git@main' \"\n", " \"qwen-vl-utils accelerate pillow datasets huggingface_hub\", shell=True)\n", "subprocess.run(\"pip -q install xgrammar || echo 'xgrammar build failed -> constrained falls back'\", shell=True)\n", "subprocess.run(\"pip -q install causal-conv1d || echo 'causal-conv1d optional'\", shell=True)\n", "\n", "# 2) the full array of natively-fitting models (edit to taste). 0.8B pair = the finetune floor.\n", "MODELS = [\n", " \"qwen3.5-0.8b\", \"qwen3.5-0.8b-json-captioner\",\n", " \"qwen3.5-2b\", \"qwen3.5-4b\", \"qwen3.5-9b\", \"qwen3.5-27b\", \"qwen3.5-35b-a3b\",\n", " \"qwen3vl-2b\", \"qwen3vl-4b\", \"qwen3vl-8b\", \"qwen3vl-32b\", \"qwen3vl-30b-a3b\",\n", "]\n", "from qwen_test_runner.vision.tasks_vision import category_names\n", "CATS = category_names() # all 15\n", "N = 12\n", "OUT = \"/content/runs_vlm\"\n", "\n", "# 3) run the CLI (durable + cache-clearing). Streams progress live; re-run to resume.\n", "cmd = [\"python\", \"-m\", \"qwen_test_runner.vision.run_vlmbench\",\n", " \"--runner\", \"vlm\", \"--dataset\", \"full\",\n", " \"--models\", *MODELS, \"--categories\", *CATS,\n", " \"--modes\", \"json_mode\", \"constrained\",\n", " \"--n\", str(N), \"--clear-cache-after-model\", \"--output-root\", OUT]\n", "print(\"RUNNING:\", \" \".join(cmd), \"\\n\")\n", "subprocess.run(cmd)\n", "\n", "# 4) consolidate: leaderboard + the full category x model effective-yield matrix (json_mode)\n", "runs = sorted(glob.glob(f\"{OUT}/*/\"))\n", "if runs:\n", " rd = runs[-1]\n", " lb = os.path.join(rd, \"leaderboard.md\")\n", " if os.path.exists(lb):\n", " print(\"\\n================ LEADERBOARD ================\\n\")\n", " print(open(lb, encoding=\"utf-8\").read())\n", " # full matrix from metrics.jsonl: effective yield = primary_score_mean * schema_valid_rate\n", " mrows = []\n", " mp = os.path.join(rd, \"metrics.jsonl\")\n", " if os.path.exists(mp):\n", " for ln in open(mp, encoding=\"utf-8\"):\n", " if ln.strip():\n", " mrows.append(json.loads(ln))\n", " jm = [r for r in mrows if r.get(\"mode\") == \"json_mode\"]\n", " cats = [c for c in CATS]\n", " models = [m for m in MODELS]\n", " def eff(m, c):\n", " rr = [r for r in jm if r[\"model\"] == m and r[\"category\"] == c]\n", " if not rr:\n", " return None\n", " r = rr[0]\n", " acc = r.get(\"primary_score_mean\")\n", " return None if acc is None else acc * r.get(\"schema_valid_rate\", 0.0)\n", " print(\"\\n================ EFFECTIVE-YIELD MATRIX (category x model, json_mode) ================\\n\")\n", " short = {m: m.replace(\"qwen3.5-\", \"q35-\").replace(\"qwen3vl-\", \"vl-\").replace(\"-json-captioner\", \"-cap\")\n", " for m in models}\n", " hdr = [\"category\"] + [short[m] for m in models]\n", " rows = []\n", " for c in cats:\n", " rows.append([c[:22]] + [(\"n/a\" if eff(m, c) is None else f\"{eff(m, c):.2f}\") for m in models])\n", " means = [\"MEAN\"]\n", " for m in models:\n", " vals = [eff(m, c) for c in cats if eff(m, c) is not None]\n", " means.append(f\"{(sum(vals)/len(vals)):.3f}\" if vals else \"n/a\")\n", " rows.append(means)\n", " w = [max(len(str(r[i])) for r in ([hdr] + rows)) for i in range(len(hdr))]\n", " print(\" \".join(hdr[i].ljust(w[i]) for i in range(len(hdr))))\n", " print(\" \".join(\"-\" * w[i] for i in range(len(hdr))))\n", " for r in rows:\n", " print(\" \".join(str(r[i]).ljust(w[i]) for i in range(len(hdr))))\n", " print(f\"\\nrun_dir: {rd}\")\n", " print(\"Paste BOTH the LEADERBOARD and the EFFECTIVE-YIELD MATRIX back (this is the full article dataset).\")\n", "else:\n", " print(\"no run dir found — check the CLI output above for errors.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4_FDwbaeAHO6", "outputId": "31ba8988-7523-43d2-9a35-9b9cd7c8e5fe" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "unzipped: qwen_vlmbench_colab_v8.zip -> /content/qwen-test-runner\n", "contents: ['LICENSE', 'README.md', 'pyproject.toml', 'qwen_test_runner', 'tests']\n", "\n", "$ pip -q install pydantic pytest\n", "\n", "$ pip -q install --no-deps -e /content/qwen-test-runner\n", "\n", "$ python -m pytest tests/test_vision_registry.py tests/test_vision_coords.py tests/test_vision_metrics.py tests/test_vision_bench.py -q\n", "\n", "$ python -m qwen_test_runner.vision.run_vlmbench --runner stub --dataset smoke --models qwen3.5-9b qwen3.5-0.8b-json-captioner --categories image_classification bbox_grounding ocr_text --modes json_mode constrained --n 3 --output-root /content/runs_vision\n", "\n", "================= LEADERBOARD =================\n", "\n", "# Qwen VLM Labeler Selection — 20260629T201204_238900Z\n", "\n", "models=['qwen3.5-9b', 'qwen3.5-0.8b-json-captioner'] categories=3 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=3 dataset=smoke runner=stub\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 100.0% | 100.0% | 0.0% | 1.000 | 23389 | native_capable |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3.5-9b | instruct | 1.000 | 3600000 | 1.000 |\n", "| 2 | qwen3.5-0.8b-json-captioner | instruct | 1.000 | 3600000 | 1.000 |\n", "\n", "## Headline recommendation\n", "\n", "> **Best no-finetune labeler:** `qwen3.5-9b` (instruct) — 100.0% native-valid, 1.000 accuracy, 23389 tok/s. Ships as-is.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3.5-9b` (instruct) — 3600000 samples/hr at labeler 1.000.\n", "\n", "\n", "$ nvidia-smi -L\n", "\n", "$ python -c \"import torch; print('torch', torch.__version__, '| cuda', torch.cuda.is_available(), '|', (torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'))\"\n", "\n", "ESTABLISHED: qwen_test_runner + vision harness installed, 44 vision tests pass, stub benchmark ran and produced a leaderboard on this machine.\n", "RUNNING: python -m qwen_test_runner.vision.run_vlmbench --runner vlm --dataset full --models qwen3.5-0.8b qwen3.5-0.8b-json-captioner qwen3.5-2b qwen3.5-4b qwen3.5-9b qwen3.5-27b qwen3.5-35b-a3b qwen3vl-2b qwen3vl-4b qwen3vl-8b qwen3vl-32b qwen3vl-30b-a3b --categories image_classification bbox_grounding ocr_text data_type_differentiation data_type_utilization structural_spatial_awareness depth_analysis subject_fixation segmentation outline_association geometric_3d_object_id camera_rotational_offset vit_accuracy_to_prompt semantic_association style_structural_awareness --modes json_mode constrained --n 12 --clear-cache-after-model --output-root /content/runs_vlm \n", "\n", "\n", "================ LEADERBOARD ================\n", "\n", "# Qwen VLM Labeler Selection — 20260629T201349_323268Z\n", "\n", "models=['qwen3.5-0.8b', 'qwen3.5-0.8b-json-captioner', 'qwen3.5-2b', 'qwen3.5-4b', 'qwen3.5-9b', 'qwen3.5-27b', 'qwen3.5-35b-a3b', 'qwen3vl-2b', 'qwen3vl-4b', 'qwen3vl-8b', 'qwen3vl-32b', 'qwen3vl-30b-a3b'] categories=15 reasoning=['instruct'] modes=['json_mode', 'constrained'] n=12 dataset=full runner=vlm\n", "\n", "## Quality leaderboard (labeler_score, native json_mode)\n", "\n", "| rank | model | reason | acc | native_valid | native_robust | gap | labeler | tok/s | bucket |\n", "|------|-------|--------|-----|--------------|---------------|-----|---------|-------|--------|\n", "| 1 | qwen3.5-27b | instruct | 0.638 | 82.8% | 82.8% | 4.4% | 0.553 | 19 | finetune_candidate |\n", "| 2 | qwen3.5-35b-a3b | instruct | 0.645 | 78.9% | 78.9% | 6.1% | 0.541 | 27 | finetune_candidate |\n", "| 3 | qwen3vl-4b | instruct | 0.597 | 84.4% | 84.4% | 2.2% | 0.524 | 73 | finetune_candidate |\n", "| 4 | qwen3vl-32b | instruct | 0.613 | 80.6% | 80.6% | 6.7% | 0.521 | 18 | finetune_candidate |\n", "| 5 | qwen3vl-8b | instruct | 0.579 | 82.8% | 82.8% | 6.7% | 0.502 | 57 | finetune_candidate |\n", "| 6 | qwen3vl-30b-a3b | instruct | 0.600 | 78.3% | 78.3% | 8.3% | 0.500 | 36 | finetune_candidate |\n", "| 7 | qwen3.5-4b | instruct | 0.611 | 76.1% | 68.3% | 9.4% | 0.487 | 49 | finetune_candidate |\n", "| 8 | qwen3.5-9b | instruct | 0.552 | 79.4% | 76.7% | 7.8% | 0.461 | 48 | finetune_candidate |\n", "| 9 | qwen3vl-2b | instruct | 0.451 | 82.8% | 82.8% | 8.3% | 0.391 | 89 | insufficient |\n", "| 10 | qwen3.5-2b | instruct | 0.384 | 71.1% | 71.1% | 15.6% | 0.300 | 66 | insufficient |\n", "| 11 | qwen3.5-0.8b | instruct | 0.219 | 58.3% | 33.9% | 17.8% | 0.139 | 68 | insufficient |\n", "| 12 | qwen3.5-0.8b-json-captioner | instruct | 0.241 | 46.7% | 0.0% | 27.2% | 0.123 | 70 | insufficient |\n", "\n", "## Fleet leaderboard (accuracy × throughput)\n", "\n", "| rank | model | reason | labeler | samples/hr | fleet |\n", "|------|-------|--------|---------|------------|-------|\n", "| 1 | qwen3vl-4b | instruct | 0.524 | 2290 | 0.375 |\n", "| 2 | qwen3vl-8b | instruct | 0.502 | 1790 | 0.347 |\n", "| 3 | qwen3vl-30b-a3b | instruct | 0.500 | 1086 | 0.323 |\n", "| 4 | qwen3.5-4b | instruct | 0.487 | 1166 | 0.318 |\n", "| 5 | qwen3.5-35b-a3b | instruct | 0.541 | 532 | 0.314 |\n", "| 6 | qwen3.5-27b | instruct | 0.553 | 365 | 0.302 |\n", "| 7 | qwen3.5-9b | instruct | 0.461 | 1132 | 0.299 |\n", "| 8 | qwen3vl-32b | instruct | 0.521 | 404 | 0.289 |\n", "| 9 | qwen3vl-2b | instruct | 0.391 | 2878 | 0.288 |\n", "| 10 | qwen3.5-2b | instruct | 0.300 | 1831 | 0.208 |\n", "| 11 | qwen3.5-0.8b | instruct | 0.139 | 1302 | 0.092 |\n", "| 12 | qwen3.5-0.8b-json-captioner | instruct | 0.123 | 2342 | 0.089 |\n", "\n", "## Headline recommendation\n", "\n", "> **No natively-sufficient model.** Best **finetune-candidate:** `qwen3.5-27b` (instruct) — robust vision (0.638 acc) but native JSON gap = 4.4%. Close it with the existing data-gen → SFT/LoRA pipeline.\n", "\n", "> **Best fleet labeler (1M+ images):** `qwen3vl-4b` (instruct) — 2290 samples/hr at labeler 0.524.\n", "\n", "\n", "================ EFFECTIVE-YIELD MATRIX (category x model, json_mode) ================\n", "\n", "category q35-0.8b q35-0.8b-cap q35-2b q35-4b q35-9b q35-27b q35-35b-a3b vl-2b vl-4b vl-8b vl-32b vl-30b-a3b\n", "---------------------- -------- ------------ ------ ------ ------ ------- ----------- ----- ----- ----- ------ ----------\n", "image_classification 0.00 0.00 0.00 0.33 0.42 0.42 0.50 0.08 0.00 0.17 0.33 0.42 \n", "bbox_grounding n/a n/a 0.16 0.08 0.25 0.18 0.20 0.27 0.30 0.23 0.18 0.19 \n", "ocr_text n/a n/a 0.18 0.42 0.50 0.33 0.42 0.42 0.42 0.34 0.33 0.42 \n", "data_type_differentiat 0.17 n/a 0.17 0.67 0.83 1.00 0.92 0.67 0.67 0.75 1.00 0.58 \n", "data_type_utilization 0.08 n/a 0.17 0.08 0.08 0.17 0.17 0.00 0.67 0.08 0.17 0.17 \n", "structural_spatial_awa 0.00 0.00 0.76 0.00 0.83 0.71 0.81 0.65 0.80 0.82 0.65 1.00 \n", "depth_analysis 0.06 0.00 0.17 0.81 1.00 1.00 1.00 0.14 0.50 0.81 1.00 1.00 \n", "subject_fixation 0.00 n/a n/a 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 \n", "segmentation 0.00 0.00 0.00 0.00 0.00 0.39 0.16 0.01 0.42 0.51 0.22 0.00 \n", "outline_association n/a 0.00 0.00 0.33 0.00 0.17 0.00 0.00 0.00 0.33 n/a 0.12 \n", "geometric_3d_object_id 0.00 0.00 n/a n/a 0.00 0.00 n/a 0.00 n/a 0.03 0.00 n/a \n", "camera_rotational_offs 0.47 0.81 0.53 0.56 0.78 0.81 0.81 0.75 0.75 0.69 0.58 0.67 \n", "vit_accuracy_to_prompt 0.50 0.25 0.75 0.92 0.50 0.92 1.00 0.25 0.58 0.67 0.92 0.50 \n", "semantic_association 0.03 0.00 0.46 0.78 0.77 0.81 0.78 0.56 0.75 0.60 0.82 0.64 \n", "style_structural_aware 0.58 0.17 0.42 0.33 0.33 0.33 0.33 0.42 0.58 0.58 0.33 0.17 \n", "MEAN 0.157 0.122 0.289 0.450 0.487 0.549 0.578 0.347 0.531 0.508 0.539 0.491 \n", "\n", "run_dir: /content/runs_vlm/20260629T201349_323268Z/\n", "Paste BOTH the LEADERBOARD and the EFFECTIVE-YIELD MATRIX back (this is the full article dataset).\n" ] } ] } ] }