{
  "tokens_out": 4899,
  "outcome": "needs_credentials",
  "suggested_fix": "Use the normal credential setup for a future live run; keep the credential-free check results.",
  "cycle_checks": null,
  "fault": {
    "owner": "prerequisite",
    "status": "diagnosed",
    "reason": "missing prerequisite cannot prove a working operation: {\"returncode\": 0, \"stdout\": \"\", \"stderr\": \"uantizing model.layers.12:  43%|\\u2588\\u2588\\u2588\\u2588\\u258e     | 12/28 [00:03<00:04,  3.68it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.12:  46%|\\u2588\\u2588\\u2588\\u2588\\u258b     | 13/28 [00:03<00:04,  3.72it/s]\\rQuantizing model.layers.13:  46%|\\u2588\\u2588\\u2588\\u2588\\u258b     | 13/28 [00:03<00:04,  3.72it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.13:  50%|\\u2588\\u2588\\u2588\\u2588\\u2588     | 14/28 [00:03<00:03,  3.81it/s]\\rQuantizing model.layers.14:  50%|\\u2588\\u2588\\u2588\\u2588\\u2588     | 14/28 [00:03<00:03,  3.81it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.14:  54%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e    | 15/28 [00:04<00:03,  3.88it/s]\\rQuantizing model.layers.15:  54%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e    | 15/28 [00:04<00:03,  3.88it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.15:  57%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b    | 16/28 [00:04<00:03,  3.84it/s]\\rQuantizing model.layers.16:  57%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b    | 16/28 [00:04<00:03,  3.84it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.16:  61%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588    | 17/28 [00:04<00:02,  3.74it/s]\\rQuantizing model.layers.17:  61%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588    | 17/28 [00:04<00:02,  3.74it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.17:  64%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258d   | 18/28 [00:04<00:02,  3.80it/s]\\rQuantizing model.layers.18:  64%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258d   | 18/28 [00:04<00:02,  3.80it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.18:  68%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a   | 19/28 [00:05<00:02,  3.90it/s]\\rQuantizing model.layers.19:  68%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a   | 19/28 [00:05<00:02,  3.90it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.19:  71%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f  | 20/28 [00:05<00:02,  3.88it/s]\\rQuantizing model.layers.20:  71%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f  | 20/28 [00:05<00:02,  3.88it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.20:  75%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c  | 21/28 [00:05<00:01,  3.91it/s]\\rQuantizing model.layers.21:  75%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c  | 21/28 [00:05<00:01,  3.91it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.21:  79%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a  | 22/28 [00:05<00:01,  3.94it/s]\\rQuantizing model.layers.22:  79%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a  | 22/28 [00:05<00:01,  3.94it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.22:  82%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f | 23/28 [00:06<00:01,  3.94it/s]\\rQuantizing model.layers.23:  82%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f | 23/28 [00:06<00:01,  3.94it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.23:  86%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c | 24/28 [00:06<00:01,  3.93it/s]\\rQuantizing model.layers.24:  86%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c | 24/28 [00:06<00:01,  3.93it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.24:  89%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2589 | 25/28 [00:06<00:00,  3.84it/s]\\rQuantizing model.layers.25:  89%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2589 | 25/28 [00:06<00:00,  3.84it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.25:  93%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e| 26/28 [00:06<00:00,  3.87it/s]\\rQuantizing model.layers.26:  93%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e| 26/28 [00:06<00:00,  3.87it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.26:  96%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b| 27/28 [00:07<00:00,  3.73it/s]\\rQuantizing model.layers.27:  96%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b| 27/28 [00:07<00:00,  3.73it/s]\\u001b[38;20m2026-10-18 18:48:20 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.27: 100%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588| 28/28 [00:07<00:00,  3.79it/s]\\u001b[38;20m2026-10-18 18:48:20 INFO shard_writer.py L527: model has been saved to ./tmp_autoround/Qwen3-0.6B-w4g128/\\u001b[0m\\n\\rQuantizing model.layers.27: 100%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588| 28/28 [00:08<00:00,  3.44it/s]\\n\\u001b[38;20m2026-10-18 18:48:20 INFO utils.py L426: Immediate saving mode: weights already saved by ShardWriter, saving configs only.\\u001b[0m\\n\\u001b[38;20m2026-10-18 18:48:21 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\", \"needs_credentials\": true, \"needs_browser\": false}",
    "evidence": []
  },
  "runtime_revision": null,
  "agent_execution": "native_agent",
  "time_to_working_s": null,
  "usage_complete": true,
  "runtime": null,
  "architecture": null,
  "local_or_hosted": null,
  "agent_interfaces_note": null,
  "platform_details": null,
  "platforms_claimed": null,
  "maintenance": null,
  "evidence_strength": null,
  "purpose": "initial",
  "topic": null,
  "needs_which": null,
  "tested_with": null,
  "native_bundle": null,
  "agent_runtime": "claude-code",
  "maintenance_note": null,
  "runtime_version": null,
  "does_what_it_says": null,
  "commit": "ec10a649b073a084af79d2c2986e92840739d0df",
  "failure_origin": null,
  "stuck_step": "<host-path> --model Qwen/Qwen3-0.6B --scheme W4A16",
  "repo_url": null,
  "needs": null,
  "checks": [
    {
      "check": "operation",
      "status": "FAIL",
      "details": {
        "working": false,
        "argv": [
          "--model",
          "Qwen/Qwen3-0.6B",
          "--scheme",
          "W4A16"
        ],
        "kind": "cli",
        "returncode": 0,
        "stdout": "",
        "stderr": "uantizing model.layers.12:  43%|\u2588\u2588\u2588\u2588\u258e     | 12/28 [00:03<00:04,  3.68it/s][38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB[0m\n\nQuantizing model.layers.12:  46%|\u2588\u2588\u2588\u2588\u258b     | 13/28 [00:03<00:04,  3.72it/s]\nQuantizing model.layers.13:  46%|\u2588\u2588\u2588\u2588\u258b     | 13/28 [00:03<00:04,  3.72it/s][38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB[0m\n\nQuantizing model.layers.13:  50%|\u2588\u2588\u2588\u2588\u2588     | 14/28 [00:03<00:03,  3.81it/s]\nQuantizing model.layers.14:  50%|\u2588\u2588\u2588\u2588\u2588     | 14/28 [00:03<00:03,  3.81it/s][38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB[0m\n\nQuantizing model.layers.14:  54%|\u2588\u2588\u2588\u2588\u2588\u258e    | 15/28 [00:04<00:03,  3.88it/s]\nQuantizing model.layers.15:  54%|\u2588\u2588\u2588\u2588\u2588\u258e    | 15/28 [00:04<00:03,  3.88it/s][38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB[0m\n\nQuantizing model.layers.15:  57%|\u2588\u2588\u2588\u2588\u2588\u258b    | 16/28 [00:04<00:03,  3.84it/s]\n[excerpt truncated]",
        "needs_credentials": true,
        "needs_browser": false,
        "blocker": "missing prerequisite cannot prove a working operation: {\"returncode\": 0, \"stdout\": \"\", \"stderr\": \"uantizing model.layers.12:  43%|\\u2588\\u2588\\u2588\\u2588\\u258e     | 12/28 [00:03<00:04,  3.68it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.12:  46%|\\u2588\\u2588\\u2588\\u2588\\u258b     | 13/28 [00:03<00:04,  3.72it/s]\\rQuantizing model.layers.13:  46%|\\u2588\\u2588\\u2588\\u2588\\u258b     | 13/28 [00:03<00:04,  3.72it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.13:  50%|\\u2588\\u2588\\u2588\\u2588\\u2588     | 14/28 [00:03<00:03,  3.81it/s]\\rQuantizing model.layers.14:  50%|\\u2588\\u2588\\u2588\\u2588\\u2588     | 14/28 [00:03<00:03,  3.81it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.14:  54%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e    | 15/28 [00:04<00:03,  3.88it/s]\\rQuantizing model.layers.15:  54%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e    | 15/28 [00:04<00:03,  3.88it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.15:  57%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b    | 16/28 [00:04<00:03,  3.84it/s]\\rQuantizing model.layers.16:  57%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b    | 16/28 [00:04<00:03,  3.84it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.16:  61%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588    | 17/28 [00:04<00:02,  3.74it/s]\\rQuantizing model.layers.17:  61%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588    | 17/28 [00:04<00:02,  3.74it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.17:  64%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258d   | 18/28 [00:04<00:02,  3.80it/s]\\rQuantizing model.layers.18:  64%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258d   | 18/28 [00:04<00:02,  3.80it/s]\\u001b[38;20m2026-10-18 18:48:17 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.18:  68%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a   | 19/28 [00:05<00:02,  3.90it/s]\\rQuantizing model.layers.19:  68%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a   | 19/28 [00:05<00:02,  3.90it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.19:  71%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f  | 20/28 [00:05<00:02,  3.88it/s]\\rQuantizing model.layers.20:  71%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f  | 20/28 [00:05<00:02,  3.88it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.20:  75%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c  | 21/28 [00:05<00:01,  3.91it/s]\\rQuantizing model.layers.21:  75%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c  | 21/28 [00:05<00:01,  3.91it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.21:  79%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a  | 22/28 [00:05<00:01,  3.94it/s]\\rQuantizing model.layers.22:  79%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258a  | 22/28 [00:05<00:01,  3.94it/s]\\u001b[38;20m2026-10-18 18:48:18 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.22:  82%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f | 23/28 [00:06<00:01,  3.94it/s]\\rQuantizing model.layers.23:  82%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258f | 23/28 [00:06<00:01,  3.94it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.23:  86%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c | 24/28 [00:06<00:01,  3.93it/s]\\rQuantizing model.layers.24:  86%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258c | 24/28 [00:06<00:01,  3.93it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.24:  89%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2589 | 25/28 [00:06<00:00,  3.84it/s]\\rQuantizing model.layers.25:  89%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2589 | 25/28 [00:06<00:00,  3.84it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.25:  93%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e| 26/28 [00:06<00:00,  3.87it/s]\\rQuantizing model.layers.26:  93%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258e| 26/28 [00:06<00:00,  3.87it/s]\\u001b[38;20m2026-10-18 18:48:19 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.26:  96%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b| 27/28 [00:07<00:00,  3.73it/s]\\rQuantizing model.layers.27:  96%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u258b| 27/28 [00:07<00:00,  3.73it/s]\\u001b[38;20m2026-10-18 18:48:20 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers.27: 100%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588| 28/28 [00:07<00:00,  3.79it/s]\\u001b[38;20m2026-10-18 18:48:20 INFO shard_writer.py L527: model has been saved to ./tmp_autoround/Qwen3-0.6B-w4g128/\\u001b[0m\\n\\rQuantizing model.layers.27: 100%|\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588\\u2588| 28/28 [00:08<00:00,  3.44it/s]\\n\\u001b[38;20m2026-10-18 18:48:20 INFO utils.py L426: Immediate saving mode: weights already saved by ShardWriter, saving configs only.\\u001b[0m\\n\\u001b[38;20m2026-10-18 18:48:21 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\", \"needs_credentials\": true, \"needs_browser\": false}"
      }
    }
  ],
  "open_issues": null,
  "agent_runtime_version": "2.1.300 (Claude Code)",
  "verifies_run_id": null,
  "total_s": 620.4572415351868,
  "license": null,
  "tool_calls": 9,
  "model_version": "claude-haiku-5-5",
  "example_checks": [],
  "mcp": null,
  "other_model": null,
  "first_use_s": null,
  "native_tool_calls": 9,
  "last_commit_at": null,
  "source": "https://github.com/intel/auto-round",
  "models_agree": null,
  "maintenance_verdict": null,
  "cost_usd": 0.00916573,
  "safety": null,
  "answered_issues": null,
  "what_failed": null,
  "steps": [
    "cd <host-path> && ls -la && cat README.md | head -200",
    "ls -la <host-path> 2>/dev/null; ls -la <host-path> 2>/dev/null; python3 --version; which pip pip3 uv node npm 2>&1",
    "cd <host-path> && grep -n \"^#\" README.md | head -60; sed -n 200,260p README.md; cat requirements.txt; grep -n \"argv\\|kind\" <host-path> | head -80",
    "(sudo -n true && echo SUDO_OK); cat <host-path>; echo; export DEBIAN_FRONTEND=noninteractive; sudo apt-get update -qq >/tmp/apt.log 2>&1; sudo apt-get install -y -qq python3-pip python3-venv >>/tmp/apt.log 2>&1; echo apt=$?; python3 -m venv <host-path> && echo venv_ok",
    "cd <host-path> && export NODE_USE_ENV_PROXY=1 && timeout 1500 <host-path> install -q . > /tmp/pip_install.log 2>&1; echo rc=$?; tail -30 /tmp/pip_install.log; ls <host-path> | grep -i round",
    "cd /tmp && timeout 20 curl -sI https://huggingface.co/Qwen/Qwen3-0.6B/resolve/main/config.json | head -3; echo rc=$?; env | grep -i -E \"proxy|HF_|CDP\" | sed 's/=.*@/=***@/'; cd <host-path> && git status --short 2>/dev/null | head; ls -d build *.egg-info 2>/dev/null",
    "cd <host-path> && export NODE_USE_ENV_PROXY=1 && time timeout 1500 <host-path> --model Qwen/Qwen3-0.6B --scheme \"W4A16\" > /tmp/rtn.log 2>&1; echo rc=$?; tail -25 /tmp/rtn.log",
    "cd <host-path> && ls -la tmp_autoround/Qwen3-0.6B-w4g128/ && mkdir -p .coldpass && cat > .coldpass/probe.json <<'EOF'\n{\"kind\":\"cli\",\"argv\":[\"<host-path>\",\"--model\",\"Qwen/Qwen3-0.6B\",\"--scheme\",\"W4A16\"],\"cwd\":\"<host-path>\",\"env\":{\"NODE_USE_ENV_PROXY\":\"1\"}}\nEOF\ncat > .coldpass/report.json <<'EOF'\n{\"outcome\":\"working\",\"operation\":\"Quantize Qwen/Qwen3-0.6B to W4A16 with the README 'Other Recipes' command auto-round-rtn (pure RTN) and save the quantized model to ./tmp_autoround/Qwen3-0.6B-w4g128/\",\"operation_scope\":\"CLI quantization of a small HF model on CPU; verified the save message 'model has been saved to ./tmp_autoround/Qwen3-0.6B-w4g128/'. Not exercised: inference with the saved model, vLLM/SGLang/Transformers runtime, GPU/XPU/HPU paths, the test suite, and lint (both skipped per task).\",\"steps\":[\"apt-get install python3-pip python3-venv (prerequisite; pip was missing)\",\"python3 -m venv <host-path>\",\"cd <host-path> && <host-path> install . (README 'Build from Source', CPU path; NODE_USE_ENV_PROXY=1 exported)\",\"<host-path> --model Qwen/Qwen3-0.6B --scheme W4A16 (README 'Other Recipes' block) -> rc=0, saved to ./tmp_autoround/Qwen3-0.6B-w4g128/ in ~33s\"],\"deviations\":[\"pip and venv were not present in the guest; installed python3-pip and python3-venv via apt and used a venv at <host-path> rather than system pip\"],\"stuck_step\":null,\"main_blocker\":null,\"suggested_fix\":null}\nEOF\n<env-var>=<host-path> python3 -I <host-path> --validate; echo rc=$?",
    "cd <host-path> && rm -rf tmp_autoround build auto_round.egg-info && <env-var>=<host-path> python3 -I <host-path> --validate; echo rc=$?; ls -la .coldpass; pgrep -fa auto-round || echo no_leftover_procs"
  ],
  "platform_conflicts": null,
  "claims_note": null,
  "maintained_note": null,
  "repo": "intel/auto-round",
  "service_tier": null,
  "maintained": null,
  "source_pin": null,
  "operation_scope": "CLI quantization of a small HF model on CPU; verified the save message 'model has been saved to ./tmp_autoround/Qwen3-0.6B-w4g128/'. Not exercised: inference with the saved model, vLLM/SGLang/Transformers runtime, GPU/XPU/HPU paths, the test suite, and lint (both skipped per task).",
  "reasoning": "low",
  "claims": [],
  "platform": "linux",
  "cost_to_try": null,
  "backend": null,
  "tool_transport": "mcp-assigned-vm",
  "hardware": null,
  "agent_interface": null,
  "profile": {
    "model": "claude-haiku-5-5",
    "reasoning": "low"
  },
  "date": "2026-10-18T18:48:23.936661+00:00",
  "kind": "repo",
  "resources": {
    "cpus": 2,
    "memory_mib": 4096,
    "disk_mib": 32768
  },
  "error_excerpt": "missing prerequisite cannot prove a working operation: {\"returncode\": 0, \"stdout\": \"\", \"stderr\": \"uantizing model.layers.12: 43%|\\u2588\\u2588\\u2588\\u2588\\u258e | 12/28 [00:03<00:04, 3.68it/s]\\u001b[38;20m2026-10-18 18:48:16 INFO device.py L1553: 'peak_ram': 3.33GB\\u001b[0m\\n\\rQuantizing model.layers",
  "pass": null,
  "evidence_version": null,
  "source_revision": null,
  "run_id": "intel--auto-round-claude-haiku-5-5-1792348683479401658",
  "last_release_at": null,
  "furthest_model": null,
  "model": "claude-haiku-5-5",
  "what_it_did": "Quantize Qwen/Qwen3-0.6B to W4A16 with the README 'Other Recipes' command auto-round-rtn (pure RTN) and save the quantized model to ./tmp_autoround/Qwen3-0.6B-w4g128/",
  "sandbox_source": null,
  "other_run_id": null,
  "hosting": null,
  "tested_commit": null,
  "guest_setup": null,
  "mcp_context": null,
  "agent_interfaces": [],
  "setup_identity": null,
  "works": null,
  "install_s": null,
  "fix_id": null,
  "tokens_in": 178601,
  "tested_at": null,
  "test_version": "readme-80bce05978a913d9396742111125106710e3c4b0dc4098ba6143b35960c73717",
  "recovery": {
    "source": "legacy-oct16-18",
    "original_run_id": "intel--auto-round-claude-haiku-5-5-1792348683479401658",
    "historical": true,
    "ledger_sha256": "c5e6a833c182c1e2aecf69d0bd0020aec63c65d98f3e933e4b32232340312736"
  },
  "transcript_url": "/transcripts/intel--auto-round-claude-haiku-5-5-1792348683479401658.txt",
  "report_url": "/reports/intel--auto-round-claude-haiku-5-5-1792348683479401658.json"
}