CHINAAI QWEN LOCAL WINDOWS EXPERIMENT — 2026-09-21 Plain-text companion to the 39-file evidence ZIP. Each named section preserves the UTF-8 content of its archive entry. ===== FILE: README.md ===== # ChinaAI: Qwen local Windows test records Companion guide: https://chinaai.chat/qwen/run-locally/ This is an evidence archive, not an installer or a general model benchmark. It contains the fixed fictional prompts, first responses, repeated responses and measurements behind the guide. No runtime, model weights, API credentials or private server logs are included. ## What was tested - Native Ollama package: `qwen3.5:9b`; the resolved manifest digest and model metadata are included. - Runtime: standalone Ollama 0.34.2 for Windows x86-64. - Tasks: strict JSON extraction, a three-bullet factual summary and short integer arithmetic. - Repetition: three total requests per task. Each task starts with the model unloaded, followed by two requests with the model resident. Windows' file cache is not cleared. Repeated identical prompts can benefit from prompt caching. - Settings: thinking disabled; 4,096-token context; at most 256 generated tokens per task; eight inference threads; temperature 0; seed 42. Other native-package defaults remain in effect and are recorded separately. - The two preliminary smoke requests and separate nonstreaming PowerShell check use a 128-token output limit. They are excluded from the nine-request timing table. - Cloud features are disabled. The server binds to the computer's loopback address. The computer was not disconnected from the internet; this is not a network-isolation audit. - Vulkan is disabled for this CPU-oriented experiment. The recorded `ollama ps` result identifies actual placement. ## Read the records `fixtures.json` contains the exact tasks, settings and criteria defined before inference. `fixture-freeze.json` records their pre-inference fingerprint. The archive's `SHA256SUMS.txt` fingerprints the published files for download-integrity checks; it is not a digital signature or proof of independent authorship. `extraction-1.json`, `summary-1.json` and `arithmetic-1.json` preserve the first answer for each task. Files ending in `-2` and `-3` are the two resident repeats. Each request record includes the exact request, unedited response, streamed response events, completion metadata, elapsed client time and sampled process-memory figures. A seed and zero temperature do not guarantee identical output across different runtime builds or hardware. `mechanical-review.json` checks strict JSON, the arithmetic answer, summary formatting, request settings and output limits. Summary factual coverage additionally requires the separate semantic assessment in `answer-review.md`; formatting alone is not a quality pass. `measurement-harness.py.txt` and `mechanical-checks.py.txt` are reference snapshots of our measurement and mechanical-check code. They make the timing, sampling and scoring methods inspectable. They depend on our original project layout and locally prepared runtime, so they are supplied as text for inspection, not ready-to-run scripts. Use the manual request below to repeat a task on your own configured server. `request-metrics.csv` contains all nine measured requests. `timing-summary.json` groups those same observations by task. `placement-cli.json`, `walkthrough-cli.json` and `walkthrough-stop-cli.json` contain captured native CLI output. The actual initial model download was made through Ollama's native pull API; the walkthrough's CLI pull syntax was checked against native help. `powershell-script-file-attempt.json` preserves the blocked `.ps1` launch, with only the private local path redacted. No inference request was sent by that blocked launch. `powershell-command-demo.json` records the successful individual-command request matching the guide. No execution-policy setting was changed. Its before/after policy fields are blank because the read-only policy-inspection command could not load its required module; they do not identify an observed policy. `execution-policy-observation.json` records a later read-only inspection failure. The first verification helper stopped at that metadata check after the successful response. The separately recorded stop check then verified `ps`, model-specific `stop`, and an empty loaded-model list. `download-recovery.json` contains selected progress fields from two connection resets performed before inference, after stalled-part messages and persistently slow remaining transfers. It excludes private process and filesystem details. Download recovery is separate from the answer tests. ## Repeat one task manually Follow the guide to start a configured local Ollama server and finish the model download. Open PowerShell in the directory containing `fixtures.json` from this archive. Select a task, then send its exact prompt and options to that server: ```powershell $fixture = Get-Content -LiteralPath '.\fixtures.json' -Raw -Encoding UTF8 | ConvertFrom-Json $task = $fixture.tasks | Where-Object { $_.id -eq 'extraction' } $request = @{ model = $fixture.model prompt = $task.prompt think = $false stream = $false keep_alive = '5m' options = $fixture.options } | ConvertTo-Json -Depth 8 $answer = Invoke-RestMethod -Uri 'http://127.0.0.1:11434/api/generate' ` -Method Post -ContentType 'application/json; charset=utf-8' ` -Body ([System.Text.Encoding]::UTF8.GetBytes($request)) -TimeoutSec 180 $answer.response ``` Change the task selector to `summary` or `arithmetic` to use either other fixed prompt. This reader-facing request uses nonstreaming output for simplicity; it does not reproduce the original streaming time-to-first-content measurement or the memory sampler. Compare the output with the original criteria, not just with whether a response arrived. To approximate the original request order, unload only the named model with `ollama stop qwen3.5:9b`, send one selected task, then send that identical task two more times before moving to the next task. Use the actual executable path for a standalone installation, as shown in the guide. Do not stop an unrelated model or server. ## Measurement limits The experiment ran on an active desktop. It does not establish a minimum RAM specification, performance on smaller machines, general reasoning accuracy, long-context capacity, vision quality or coding-agent compatibility. Client elapsed time includes waiting for the whole request. First-content time is measured to the first nonempty streamed answer text. Generation tokens per second is generated-token count divided by the runtime's reported generation duration, so it is not end-to-end response speed. The memory figure is a sampled sum of the launched server's and descendants' process working sets at nominal 250-millisecond intervals. Shared pages may be counted more than once; an unsampled peak may be missed. This is neither whole-system memory use nor the exact physical memory required by the model. Some metadata use decimal GB and some observations use binary GiB. 1 GB is 1,000,000,000 bytes; 1 GiB is 1,073,741,824 bytes. Preserve the unit when comparing numbers. If a later native model tag resolves to another digest, record it as a different tested package. Do not silently replace the original dated results. China AI Chat Team. Corrections: https://chinaai.chat/contact/ ===== FILE: SHA256SUMS.txt ===== 29b5b34956898212c04e59773fcb3c16c622d68d788b06cc96a7ae4e2fa9f012 README.md f3c6e5bee750ce46b38ed010dbd0d1c9a07febde4cc856d4872afac8e69d5310 answer-review.md da745efb6d85715ce35849f9359a28e0f1bc2012ea76f22ed435fefb72a37134 arithmetic-1.json a0c7b1aee4546b46df9758f9cb428452171324a584a285629010c5d2f9fcf345 arithmetic-2.json 244668d35dba77884434971be0154cbaaf64e5d3983c320fafdce31aaca022c4 arithmetic-3.json 2ec8d78a0467ef2808ae32843546a28966b6cc39db2c4663e6e3f3418e583815 arithmetic-before-cold.json 3938390915a73c9b85193a8750d66f955ccb1cd6e29b0e73777dcbc5ecf00779 configuration.json c54208c5eb9073acb5c500ff04e208e6110c85058d1993dedc32969c1839ee86 download-recovery.json e9a0bba0297fb7270cd4d73170900150ad6649224f3591a4b87db14d5184a3df execution-policy-observation.json 006dc3d6cd75c989420ada26442e210e580c571389b14865654d3fe836b696c5 extraction-1.json bd69e8473ec5289664afbb19da2283bc6d4deee7cd6aeeb4e63b97a25d7ea9d3 extraction-2.json b672f31153bdcd7652640ebfe5d3e4cfb4f49f1a61f41d5482a1a7ec3ae7b897 extraction-3.json 2ec8d78a0467ef2808ae32843546a28966b6cc39db2c4663e6e3f3418e583815 extraction-before-cold.json 41156812d1339c4fcd2f7e51fadb5f7b4196fe988a0f09d23e915db68fdea88a fixture-freeze.json b17420858a3810db5c234c78862261acb2fe6d473dfc0a468cbe567f4d7866bd fixtures.json 702235a06a10dbbd217731297552db9430d6d6b714a437b1a95eaf02870a4855 hardware.json 6c17b79dffb43cf580176e0b88307b067ed748376cfd1eecbbf32ebf41d2cad0 manifest.json 6786bf3d6e51993966cd178d68eaa3a272f307b167a443020bb539321c5d9b3c measurement-harness.py.txt f45d8b2174291a9a37ca0fce81432eed9a6cb9f7c3b60312c28ae0da8fb6838f mechanical-checks.py.txt 3e65142fe00a146341d7a9fc0ff9acda24a5a48c06d436ca4f1bdbb65f90b330 mechanical-review.json a5de28fd62896394a3c8e50b95d8232b36eb19846e471337c5706196035eadfa model-download-record.json ff8197eca16e96ed4c8bd22e08e8c92bc5a006fae591432ef8b4dbe0cc54ae3a model-list.json d745178e461402c2554d2d965cbc8048c2c3995e9e90d1624a93b623762a1a91 model-metadata.json 8fb7fa261141390762455a10fcded9584c03dfdead801edb64392741247f711c placement-cli.json ac29bec382c1699f7c7bb51cc223fd538ba549d703bdb2badad10a4d980fbd66 powershell-command-demo.json acf08ab025ad20f3159c6e4528d7372268fdd6acfae2ed0433391c1b4a047559 powershell-script-file-attempt.json a3bd6c2afcb5447f00ae2183c86c5991d6ca79e6f6ab6d0b1a64b969001373ae request-metrics.csv 45e7d7bffcd7d8259dd4942e51244aa4d9db678aa6cf1596eccc7c00459f4525 runtime.json c0f00becb2e843773d8beb8e39bf5e36e73bc0193590cee12d16cd229684d97d smoke-1.json c58f6fc0fb94f78ca28bcb4c919631894edf8b3165aae1c20e08bc060c9a67d9 smoke-2.json 93d8e2d3b0753c9457b22790a8e5c500e89c5e062da87bb1bb2759c14f537d8e summary-1.json 2e78046f80a101eba22a02c515a4b32231d0523bcb72a52cdd60670e3f89b535 summary-2.json 379608c60f46afc211096320f0385e2d9a7cbb3d6af9de78e48d20d9bb75637a summary-3.json 2ec8d78a0467ef2808ae32843546a28966b6cc39db2c4663e6e3f3418e583815 summary-before-cold.json 844fb507804d6cc51ce807762077b029d7e8d2ddb15bfb2dcff6a07243c92c29 timing-summary.json fb65b8016d3fa4e974ce4fefd79fa78dfb3f8bcd887e5a76e9aa8d22c491814f walkthrough-cli.json 8ac878ef6237179b093ce21cf623cbfe30e4dd79e7347c885358fabe3c0c0347 walkthrough-stop-cli.json 2ec8d78a0467ef2808ae32843546a28966b6cc39db2c4663e6e3f3418e583815 walkthrough-unloaded.json ===== FILE: answer-review.md ===== # Qwen local test: independent answer review Reviewed on 21 September 2026. This review checks the saved first responses and measurements against the criteria recorded before inference. It introduces no new model runs. The tested package was Ollama `qwen3.5:9b`, Q4_K_M, running in Ollama 0.34.2. Each request used a 4,096-token context, at most 256 output tokens, eight CPU threads, temperature 0, seed 42 and `think: false`. There were three small fictional tasks, each submitted three times as a fresh request. These are repeated examples, not nine independent capability tests. ## Answer results | Task | Full passes | What the saved responses show | | --- | --- | --- | | Structured extraction | 3/3 | Exact requested JSON keys, types and values, including `null` for the missing email. | | Three-bullet summary | 0/3 | Accurate included facts and correct format, but incomplete against the predeclared coverage criterion: all three omit the advice to bring water. | | Notebook arithmetic | 0/3 | All three return `59`; the correct count is `12 × 18 − 47 − 13 + 20 = 176`. | All three responses within each task were identical. No response was replaced with a better attempt. The first extraction response was: ```json { "event": "Harbor Code Clinic", "date": "2026-10-14", "time": "18:30", "city": "Bristol", "capacity": 24, "ticket_gbp": 12, "contact_email": null } ``` The first summary response was: ```text - Riverside Pocket Park opens October 4, 2026, with daily hours from 07:00 to 21:00. - Phase one added a 2 km walking path and 30 trees. - The playground remains closed pending inspection; no reopening date has been announced. Drinking fountains are not installed. ``` Its bullets contain 14, 11 and 17 words respectively, within the 25-word limit. It preserves the opening date, hours, completed-work quantities and uncertainty about the playground. The missing water advice makes it incomplete under the frozen criteria; the review found no invented fact in this response. The first arithmetic response was: ```text 59 ``` It meets the integer-only format but fails the numerical criterion. ## Measurements and limits For each task, the model was unloaded before run one and remained resident for runs two and three. Empty loaded-model records support the unload step. The operating-system file cache was not cleared; “unloaded” does not mean a fresh computer boot. | Task | First run after unload | Two resident runs | | --- | --- | --- | | Extraction | 24.00 seconds | 16.36–16.54 seconds | | Summary | 23.76 seconds | 15.11–15.14 seconds | | Arithmetic | 7.60 seconds | 0.68–0.77 seconds | These are client-observed complete-response times. Resident requests reused identical prompts and reported substantial prompt caching, so the difference is not solely model loading. The very short arithmetic output is also wrong; its speed is not evidence of successful arithmetic. All nine requests completed with `done: true`, reason `stop`, no recorded transport error and matching saved stream/output text. Output counts were 80, 74 and 3 tokens for extraction, summary and arithmetic respectively. None reached the output limit; the records do not support blaming truncation for the omissions or incorrect arithmetic. The placement command reported `100% CPU`; corresponding API records reported zero VRAM allocation and context length 4,096. This supports CPU execution for these runs, not a claim that the machine has no graphics device. Eight threads were chosen to limit desktop impact, not maximize throughput. The largest sampled sum of Ollama process working sets was about 6.55 GiB. Sampling occurred approximately every 250 milliseconds. This is neither an exact peak nor a minimum-RAM recommendation; summed process working sets can include shared pages. Recorded model files occupied 6,594,485,878 bytes, a separate disk measurement. Two preliminary smoke requests are excluded from the task table. The resident smoke finished in 1.11 seconds, below the predeclared 90-second fallback threshold. No task reached the 180-second deadline. The inference fallback conditions were not triggered, and no 4B comparison is claimed. A separate script-file walkthrough was blocked before inference. Individual PowerShell commands subsequently returned `Local model ready.` without a policy-changing command. Separate follow-up records verify model unloading and cleanup. These checks are excluded from the nine-request comparisons; the actual execution-policy value was not independently established. These findings apply to this package, configuration, machine and small English fixture set. They do not establish general model accuracy, multilingual quality, minimum hardware requirements or the cause of an answer error. Thinking was disabled; thinking-enabled behavior was not tested. Local execution was recorded, but a disconnected-network test was not performed. ===== FILE: arithmetic-1.json ===== { "task": "arithmetic", "run": 1, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "A fictional shop starts with 12 boxes containing 18 notebooks each. It sells 47 notebooks, donates 13, and then receives 20 additional notebooks. How many notebooks remain?\n\nReturn only the count as a base-10 integer, without units or explanation.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "59", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:58.6397948Z", "response": "5", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:58.8376527Z", "response": "9", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.0418164Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 7584993300, "load_duration": 5595720900, "prompt_eval_count": 73, "prompt_eval_cached_count": 0, "prompt_eval_duration": 1583358000, "eval_count": 3, "eval_duration": 402129000 } ], "client_seconds": 7.601370800002769, "first_content_seconds": 7.1968592000011995, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.0418164Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 7584993300, "load_duration": 5595720900, "prompt_eval_count": 73, "prompt_eval_cached_count": 0, "prompt_eval_duration": 1583358000, "eval_count": 3, "eval_duration": 402129000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7014428672, "sampled_peak_private_sum_bytes": 7984898048, "memory_sample_count": 29, "generation_tokens_per_second": 7.460292592675485, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:59:59.0448351+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: arithmetic-2.json ===== { "task": "arithmetic", "run": 2, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "A fictional shop starts with 12 boxes containing 18 notebooks each. It sells 47 notebooks, donates 13, and then receives 20 additional notebooks. How many notebooks remain?\n\nReturn only the count as a base-10 integer, without units or explanation.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "59", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.3643939Z", "response": "5", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.5968751Z", "response": "9", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.83095Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 755821400, "load_duration": 2788600, "prompt_eval_count": 73, "prompt_eval_cached_count": 69, "prompt_eval_duration": 279779000, "eval_count": 3, "eval_duration": 466708000 } ], "client_seconds": 0.7749631000006048, "first_content_seconds": 0.30650239999886253, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:54:59.83095Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 755821400, "load_duration": 2788600, "prompt_eval_count": 73, "prompt_eval_cached_count": 69, "prompt_eval_duration": 279779000, "eval_count": 3, "eval_duration": 466708000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7014510592, "sampled_peak_private_sum_bytes": 7984939008, "memory_sample_count": 3, "generation_tokens_per_second": 6.428002091243346, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:59:59.8334684+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: arithmetic-3.json ===== { "task": "arithmetic", "run": 3, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "A fictional shop starts with 12 boxes containing 18 notebooks each. It sells 47 notebooks, donates 13, and then receives 20 additional notebooks. How many notebooks remain?\n\nReturn only the count as a base-10 integer, without units or explanation.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "59", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:55:00.1239222Z", "response": "5", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:55:00.3262624Z", "response": "9", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:55:00.5236001Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 661763000, "load_duration": 3488300, "prompt_eval_count": 73, "prompt_eval_cached_count": 69, "prompt_eval_duration": 253379000, "eval_count": 3, "eval_duration": 399036000 } ], "client_seconds": 0.6767712999971991, "first_content_seconds": 0.28059159999975236, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:55:00.5236001Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 32, 42140, 7808, 8211, 440, 220, 16, 17, 14273, 8222, 220, 16, 23, 75842, 1754, 13, 1049, 29772, 220, 19, 22, 75842, 11, 1459, 942, 220, 16, 18, 11, 321, 1179, 20539, 220, 17, 15, 4945, 75842, 13, 2500, 1599, 75842, 6922, 30, 271, 5423, 1132, 279, 1698, 430, 264, 2255, 12, 16, 15, 7308, 11, 1973, 7896, 466, 15673, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 20, 24 ], "total_duration": 661763000, "load_duration": 3488300, "prompt_eval_count": 73, "prompt_eval_cached_count": 69, "prompt_eval_duration": 253379000, "eval_count": 3, "eval_duration": 399036000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7014285312, "sampled_peak_private_sum_bytes": 7984689152, "memory_sample_count": 3, "generation_tokens_per_second": 7.518118665985024, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T23:00:00.5259418+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: arithmetic-before-cold.json ===== { "models": [] } ===== FILE: configuration.json ===== { "version": { "version": "0.34.2" }, "model": "qwen3.5:9b", "cloud_disabled": true, "host": "127.0.0.1:11434", "vulkan_disabled": true, "parallel_requests": 1, "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 }, "psutil_version": "7.2.2", "date": "2026-09-21" } ===== FILE: download-recovery.json ===== [ { "record_type": "Selected non-private fields from the download-recovery record", "utc": "2026-09-21T18:59:28.099044+00:00", "reason": "Persistent stalled-part retries in native downloader; one cancel and resume attempt", "blob_digest": "sha256:dec52a44569a2a25341c4e4d3fee25846eed4f6f0b936278e3a3c900bb99d37c", "checkpointed_bytes_before_cancel": 2472923556, "checkpointed_bytes_after_cancel": 2971179928, "checkpoint_wait_seconds": 30, "server_stopped_after_checkpoint": true, "limitation": "Checkpointed progress is not a guarantee that every in-flight byte is preserved." }, { "record_type": "Selected non-private fields from the download-recovery record", "utc": "2026-09-21T19:29:48.281070+00:00", "reason": "Persistently slow remaining transfers following native stalled-part retries; bounded connection recovery", "blob_digest": "sha256:dec52a44569a2a25341c4e4d3fee25846eed4f6f0b936278e3a3c900bb99d37c", "checkpointed_bytes_before_cancel": 3659118578, "checkpointed_bytes_after_cancel": 4766476978, "checkpoint_wait_seconds": 30, "server_stopped_after_checkpoint": true, "limitation": "Checkpointed progress is not a guarantee that every in-flight byte is preserved." } ] ===== FILE: execution-policy-observation.json ===== { "note": "Later read-only diagnostic, not the original before/after observations", "exit_code": 1, "stdout": "", "stderr": "Get-ExecutionPolicy : The 'Get-ExecutionPolicy' command was found in the module 'Microsoft.PowerShell.Security', but \nthe module could not be loaded. For more information, run 'Import-Module Microsoft.PowerShell.Security'.\nAt line:1 char:1\n+ Get-ExecutionPolicy\n+ ~~~~~~~~~~~~~~~~~~~\n + CategoryInfo : ObjectNotFound: (Get-ExecutionPolicy:String) [], CommandNotFoundException\n + FullyQualifiedErrorId : CouldNotAutoloadMatchingModule\n \n" } ===== FILE: extraction-1.json ===== { "task": "extraction", "run": 1, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Extract only the facts below. Return one JSON object with exactly these keys: event, date, time, city, capacity, ticket_gbp, contact_email. Use strings for event/date/time/city, numbers for capacity/ticket_gbp, and null for missing information. No Markdown or additional text.\n\nFictional event: Harbor Code Clinic.\nDate: 14 October 2026. Start time: 18:30.\nCity: Bristol. Maximum attendance: 24 people.\nStandard ticket: GBP 12. No contact email has been published.\nUse YYYY-MM-DD for date and HH:MM for time.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "{\n \"event\": \"Harbor Code Clinic\",\n \"date\": \"2026-10-14\",\n \"time\": \"18:30\",\n \"city\": \"Bristol\",\n \"capacity\": 24,\n \"ticket_gbp\": 12,\n \"contact_email\": null\n}", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:07.7831282Z", "response": "{", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:08.4144811Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:08.6162317Z", "response": "event", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:08.8186741Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:09.0227039Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:09.2294902Z", "response": "Har", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:09.4364074Z", "response": "bor", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:09.6370464Z", "response": " Code", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:09.8396898Z", "response": " Clinic", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:10.0444047Z", "response": "\",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:10.6503535Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:10.8509781Z", "response": "date", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:11.0542563Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:11.2556172Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:11.4583032Z", "response": "2", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:11.6621935Z", "response": "0", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:11.8659102Z", "response": "2", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:12.068854Z", "response": "6", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:12.2692226Z", "response": "-", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:12.5049997Z", "response": "1", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:12.7151554Z", "response": "0", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:12.9237005Z", "response": "-", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:13.1345102Z", "response": "1", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:13.3596814Z", "response": "4", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:13.5605967Z", "response": "\",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:14.1713382Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:14.3748641Z", "response": "time", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:14.5727039Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:14.7721615Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:14.9742048Z", "response": "1", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:15.1753412Z", "response": "8", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:15.3782Z", "response": ":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:15.5761224Z", "response": "3", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:15.7802443Z", "response": "0", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:15.9820264Z", "response": "\",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:16.5903822Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:16.7900667Z", "response": "city", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:16.9972145Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:17.1962952Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:17.3982862Z", "response": "B", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:17.5997642Z", "response": "ristol", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:17.8041608Z", "response": "\",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:18.4135269Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:18.6378685Z", "response": "capacity", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:18.8407456Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:19.2556452Z", "response": " 2", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:19.4616158Z", "response": "4", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:19.6590707Z", "response": ",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:20.2685046Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:20.4721238Z", "response": "ticket", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:20.6729906Z", "response": "_g", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:20.8726524Z", "response": "bp", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:21.0740695Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:21.4793058Z", "response": " 1", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:21.6798234Z", "response": "2", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:21.8781366Z", "response": ",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:22.5160884Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:22.7270959Z", "response": "contact", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:22.9290413Z", "response": "_email", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:23.1329229Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:23.334931Z", "response": " null", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:23.738528Z", "response": "\n}", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:23.9396129Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 28014, 1132, 279, 12688, 3559, 13, 3301, 799, 4566, 1576, 440, 6681, 1439, 6679, 25, 1484, 11, 2321, 11, 854, 11, 3177, 11, 8390, 11, 11390, 1824, 17277, 11, 3522, 8899, 13, 5272, 8796, 364, 1484, 72026, 34093, 2805, 477, 11, 4947, 364, 8390, 5350, 5714, 1824, 17277, 11, 321, 819, 364, 7167, 1928, 13, 2233, 70703, 466, 4945, 1414, 13, 271, 37, 2399, 278, 1484, 25, 37885, 5927, 37926, 13, 198, 1851, 25, 220, 16, 19, 6324, 220, 17, 15, 17, 21, 13, 4980, 854, 25, 220, 16, 23, 25, 18, 15, 13, 198, 12365, 25, 42484, 13, 25749, 26151, 25, 220, 17, 19, 1208, 13, 198, 19178, 11390, 25, 80389, 220, 16, 17, 13, 2233, 3522, 2469, 682, 978, 4501, 13, 198, 9947, 58549, 17953, 38843, 364, 2321, 321, 23973, 25, 7782, 364, 854, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 90, 198, 220, 328, 2987, 763, 328, 25642, 9090, 5927, 37926, 487, 198, 220, 328, 993, 763, 328, 17, 15, 17, 21, 12, 16, 15, 12, 16, 19, 487, 198, 220, 328, 1619, 763, 328, 16, 23, 25, 18, 15, 487, 198, 220, 328, 8656, 763, 328, 33, 39276, 487, 198, 220, 328, 36151, 763, 220, 17, 19, 11, 198, 220, 328, 25686, 1824, 17277, 763, 220, 16, 17, 11, 198, 220, 328, 6089, 8899, 763, 819, 198, 92 ], "total_duration": 23975389100, "load_duration": 5583139500, "prompt_eval_count": 149, "prompt_eval_cached_count": 0, "prompt_eval_duration": 2231127000, "eval_count": 80, "eval_duration": 16157021000 } ], "client_seconds": 23.999580400002742, "first_content_seconds": 7.860895800000435, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:23.9396129Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 28014, 1132, 279, 12688, 3559, 13, 3301, 799, 4566, 1576, 440, 6681, 1439, 6679, 25, 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328, 25686, 1824, 17277, 763, 220, 16, 17, 11, 198, 220, 328, 6089, 8899, 763, 819, 198, 92 ], "total_duration": 23975389100, "load_duration": 5583139500, "prompt_eval_count": 149, "prompt_eval_cached_count": 0, "prompt_eval_duration": 2231127000, "eval_count": 80, "eval_duration": 16157021000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7036239872, "sampled_peak_private_sum_bytes": 7986245632, "memory_sample_count": 87, "generation_tokens_per_second": 4.951407812120811, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:58:23.9430679+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: extraction-2.json ===== { "task": "extraction", "run": 2, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Extract only the facts below. Return one JSON object with exactly these keys: event, date, time, city, capacity, ticket_gbp, contact_email. Use strings for event/date/time/city, numbers for capacity/ticket_gbp, and null for missing information. No Markdown or additional text.\n\nFictional event: Harbor Code Clinic.\nDate: 14 October 2026. Start time: 18:30.\nCity: Bristol. Maximum attendance: 24 people.\nStandard ticket: GBP 12. No contact email has been published.\nUse YYYY-MM-DD for date and HH:MM for time.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "{\n \"event\": \"Harbor Code Clinic\",\n \"date\": \"2026-10-14\",\n \"time\": \"18:30\",\n \"city\": \"Bristol\",\n \"capacity\": 24,\n \"ticket_gbp\": 12,\n \"contact_email\": null\n}", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:24.2248079Z", "response": "{", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:24.8375156Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:25.0430844Z", "response": "event", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:25.2458467Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:25.4515108Z", "response": " \"", "done": false }, { 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25686, 1824, 17277, 763, 220, 16, 17, 11, 198, 220, 328, 6089, 8899, 763, 819, 198, 92 ], "total_duration": 16340656100, "load_duration": 3842700, "prompt_eval_count": 149, "prompt_eval_cached_count": 145, "prompt_eval_duration": 233252000, "eval_count": 80, "eval_duration": 16098293000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7036477440, "sampled_peak_private_sum_bytes": 7986475008, "memory_sample_count": 59, "generation_tokens_per_second": 4.969470986768597, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:58:40.3270753+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: extraction-3.json ===== { "task": "extraction", "run": 3, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Extract only the facts below. Return one JSON object with exactly these keys: event, date, time, city, capacity, ticket_gbp, contact_email. Use strings for event/date/time/city, numbers for capacity/ticket_gbp, and null for missing information. No Markdown or additional text.\n\nFictional event: Harbor Code Clinic.\nDate: 14 October 2026. Start time: 18:30.\nCity: Bristol. Maximum attendance: 24 people.\nStandard ticket: GBP 12. No contact email has been published.\nUse YYYY-MM-DD for date and HH:MM for time.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "{\n \"event\": \"Harbor Code Clinic\",\n \"date\": \"2026-10-14\",\n \"time\": \"18:30\",\n \"city\": \"Bristol\",\n \"capacity\": 24,\n \"ticket_gbp\": 12,\n \"contact_email\": null\n}", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:40.6284718Z", "response": "{", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:41.2900171Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:41.4901576Z", "response": "event", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:41.6899665Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:41.8914184Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:42.095096Z", "response": "Har", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:42.2940806Z", "response": "bor", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:42.5264765Z", "response": " Code", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:42.7325027Z", "response": " Clinic", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:42.9324574Z", "response": "\",", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:43.5763913Z", "response": "\n \"", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:43.7732779Z", "response": "date", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:43.9750554Z", "response": "\":", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:44.1761116Z", "response": " \"", "done": false }, { "model": "qwen3.5:9b", 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198, 248045, 74455, 198, 248068, 271, 248069, 271, 90, 198, 220, 328, 2987, 763, 328, 25642, 9090, 5927, 37926, 487, 198, 220, 328, 993, 763, 328, 17, 15, 17, 21, 12, 16, 15, 12, 16, 19, 487, 198, 220, 328, 1619, 763, 328, 16, 23, 25, 18, 15, 487, 198, 220, 328, 8656, 763, 328, 33, 39276, 487, 198, 220, 328, 36151, 763, 220, 17, 19, 11, 198, 220, 328, 25686, 1824, 17277, 763, 220, 16, 17, 11, 198, 220, 328, 6089, 8899, 763, 819, 198, 92 ], "total_duration": 16523350900, "load_duration": 2582700, "prompt_eval_count": 149, "prompt_eval_cached_count": 145, "prompt_eval_duration": 252356000, "eval_count": 80, "eval_duration": 16262273000 } ], "client_seconds": 16.539104499999667, "first_content_seconds": 0.27480450000075507, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:53:56.8913748Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 28014, 1132, 279, 12688, 3559, 13, 3301, 799, 4566, 1576, 440, 6681, 1439, 6679, 25, 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328, 25686, 1824, 17277, 763, 220, 16, 17, 11, 198, 220, 328, 6089, 8899, 763, 819, 198, 92 ], "total_duration": 16523350900, "load_duration": 2582700, "prompt_eval_count": 149, "prompt_eval_cached_count": 145, "prompt_eval_duration": 252356000, "eval_count": 80, "eval_duration": 16262273000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7036702720, "sampled_peak_private_sum_bytes": 7986679808, "memory_sample_count": 59, "generation_tokens_per_second": 4.9193615185281905, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:58:56.8934822+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: extraction-before-cold.json ===== { "models": [] } ===== FILE: fixture-freeze.json ===== { "utc": "2026-09-21T19:30:47.997955+00:00", "sha256": "b17420858a3810db5c234c78862261acb2fe6d473dfc0a468cbe567f4d7866bd" } ===== FILE: fixtures.json ===== { "frozen_before_inference": "2026-09-21", "scope": "Three small English tasks, fresh stateless requests, three total runs per task. Not a general model benchmark.", "model": "qwen3.5:9b", "think": false, "options": {"num_ctx":4096,"num_predict":256,"temperature":0,"seed":42,"num_thread":8}, "sampling_note": "Eight CPU threads were chosen to limit impact on the active desktop, not maximize throughput. Other sampling settings retain the official package defaults and are recorded from /api/show.", "tasks": [ { "id": "extraction", "prompt": "Extract only the facts below. Return one JSON object with exactly these keys: event, date, time, city, capacity, ticket_gbp, contact_email. Use strings for event/date/time/city, numbers for capacity/ticket_gbp, and null for missing information. No Markdown or additional text.\n\nFictional event: Harbor Code Clinic.\nDate: 14 October 2026. Start time: 18:30.\nCity: Bristol. Maximum attendance: 24 people.\nStandard ticket: GBP 12. No contact email has been published.\nUse YYYY-MM-DD for date and HH:MM for time.", "expected": {"event":"Harbor Code Clinic","date":"2026-10-14","time":"18:30","city":"Bristol","capacity":24,"ticket_gbp":12,"contact_email":null}, "pass_criteria": "One valid JSON object, exact keys/types/values, no duplicate keys or surrounding text. Key order irrelevant." }, { "id": "summary", "prompt": "Summarize this fictional update in exactly three bullets, beginning each with \"- \". Use one bullet each for opening, completed work, and remaining restrictions. Each bullet must contain at most 25 words. Include the stated numbers and preserve uncertainty. Add no facts.\n\nRiverside Pocket Park opens on 4 October 2026, with daily opening hours of 07:00–21:00. Phase one added a 2 km walking path and 30 trees. The playground remains closed pending inspection; no reopening date has been announced. Drinking fountains are not installed, so visitors should bring water.", "pass_criteria": "Exactly three bullets, each <=25 whitespace-separated words. Correct opening date/hours; both quantities for completed work; playground closed pending inspection with no announced date; fountains unavailable and bring-water advice; no added facts. Semantic review required." }, { "id": "arithmetic", "prompt": "A fictional shop starts with 12 boxes containing 18 notebooks each. It sells 47 notebooks, donates 13, and then receives 20 additional notebooks. How many notebooks remain?\n\nReturn only the count as a base-10 integer, without units or explanation.", "expected": "176", "pass_criteria": "Trimmed response equals 176 exactly." } ], "run_order": "For each task: unload model, one model-unloaded run, then two resident runs. Operating system file cache is not cleared. Identical resident prompts may benefit from prompt caching.", "timeout_seconds":180, "fallback": "After diagnosis: warm smoke request capped at128 output tokens >90 seconds, request deadline reached, or persistent runtime failure. Then consider4b; preserve failed9b attempts." } ===== FILE: hardware.json ===== { "date": "2026-09-21", "cpu": "Intel(R) Core(TM) i7-14700", "logical_processors": 28, "windows_build": "26200", "windows_release": "25H2", "usable_ram_gib": 103.24, "available_ram_gib": 74.1, "smbios_installed_ram_gib": 104.0, "pci_display_adapters": [ { "description": "Intel(R) UHD Graphics 770", "name": null } ], "c_free_gib": 187.63, "cim_access": "denied; read-only registry and Windows memory API used", "limitations": "No GPU inference benchmark; integrated GPU shared memory is not dedicated VRAM. Current free RAM fluctuates." } ===== FILE: manifest.json ===== { "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:be595b49fe22012bd1f5605ec14c7ffa58331783a88a4fd8c22e5fc8ec42cf9f", "size": 475 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:dec52a44569a2a25341c4e4d3fee25846eed4f6f0b936278e3a3c900bb99d37c", "size": 6594462816 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:7339fa418c9ad3e8e12e74ad0fd26a9cc4be8703f9c110728a992b193be85cb2", "size": 11355 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:9371364b27a52acac9d87f88bd93c9db1174d8d6ec57f6888925cdc1788871ff", "size": 65 } ] } ===== FILE: measurement-harness.py.txt ===== """Local-only experiment. No API key, cloud provider or public listener.""" from pathlib import Path import datetime, hashlib, json, os, socket, subprocess, sys, threading, time, urllib.request LAB = Path(__file__).resolve().parent sys.path.insert(0, str(LAB / 'deps')) import psutil OUT = LAB.parents[1] / 'outputs' / 'qwen-local-test' OUT.mkdir(parents=True, exist_ok=True) PRIVATE = LAB / 'private' PRIVATE.mkdir(exist_ok=True) BASE = 'http://127.0.0.1:11434' LOCAL_HTTP = urllib.request.build_opener(urllib.request.ProxyHandler({})) FIXTURE = json.loads((LAB/'fixtures.json').read_text(encoding='utf-8')) MODEL = FIXTURE['model'] EXE = LAB / 'runtime' / 'ollama.exe' ENV = os.environ.copy() ENV.update(OLLAMA_HOST='127.0.0.1:11434', OLLAMA_MODELS=str(LAB/'models'), OLLAMA_NO_CLOUD='1', OLLAMA_NUM_PARALLEL='1', OLLAMA_MAX_LOADED_MODELS='1', OLLAMA_VULKAN='0', GGML_VK_VISIBLE_DEVICES='-1') # Only this child process gets an isolated Windows profile path. This avoids # accessing the real user-profile directory or changing account-wide settings. ISOLATED_PROFILE = PRIVATE / 'ollama-profile' ISOLATED_PROFILE.mkdir(exist_ok=True) ENV['USERPROFILE'] = str(ISOLATED_PROFILE) def save(name, value): target = OUT/name temp = target.with_suffix(target.suffix+'.tmp') temp.write_text(json.dumps(value, indent=2, ensure_ascii=False), encoding='utf-8') temp.replace(target) def api(route, payload=None, timeout=180): req=urllib.request.Request(BASE+route, data=None if payload is None else json.dumps(payload).encode(), headers={'Content-Type':'application/json'}) with LOCAL_HTTP.open(req, timeout=timeout) as response: return json.load(response) def cli(*args): return subprocess.run([str(EXE),*args], env=ENV, capture_output=True, text=True, timeout=30, creationflags=subprocess.CREATE_NO_WINDOW) def status(phase, **values): record={'phase':phase,'utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),**values} save('status.json',record) print(json.dumps(record),flush=True) def memory_sample(server, finish, samples): while not finish.is_set(): try: processes=[psutil.Process(server.pid)]+psutil.Process(server.pid).children(recursive=True) memories=[p.memory_info() for p in processes if p.is_running()] samples.append({'seconds':time.perf_counter(),'rss_bytes_sum':sum(m.rss for m in memories),'private_bytes_sum':sum(getattr(m,'private',0) for m in memories),'process_count':len(memories)}) except (psutil.NoSuchProcess,psutil.AccessDenied): pass finish.wait(.25) def generate(server, task_id, prompt, run, tokens=256): payload={'model':MODEL,'prompt':prompt,'stream':True,'think':False,'keep_alive':'5m','options':{**FIXTURE['options'],'num_predict':tokens}} start=time.perf_counter(); first=None; chunks=[]; final=None; error=None samples=[]; finish=threading.Event() monitor=threading.Thread(target=memory_sample,args=(server,finish,samples),daemon=True) monitor.start() response=None; deadline_hit=threading.Event() def deadline(): deadline_hit.set() try: owned=psutil.Process(server.pid) for child in owned.children(recursive=True): try: child.kill() except psutil.NoSuchProcess: pass owned.kill() except psutil.NoSuchProcess: pass timer=threading.Timer(180,deadline); timer.daemon=True; timer.start() try: req=urllib.request.Request(BASE+'/api/generate',data=json.dumps(payload).encode(),headers={'Content-Type':'application/json'}) response=LOCAL_HTTP.open(req,timeout=180) for line in response: if time.perf_counter()-start>180: raise TimeoutError('180-second operational deadline') event=json.loads(line) if event.get('error'): raise RuntimeError(event['error']) text=event.get('response','') if text and first is None: first=time.perf_counter()-start chunks.append(event) if event.get('done'): final=event if final is None: error='Stream ended without a completed response' except Exception as exc: error=type(exc).__name__+': '+str(exc) finally: elapsed=time.perf_counter()-start timer.cancel() if response is not None: response.close() finish.set(); monitor.join(2) if deadline_hit.is_set(): error='180-second watchdog terminated the owned local server and runner' result={'task':task_id,'run':run,'model':MODEL,'request':payload,'output':''.join(e.get('response','') for e in chunks),'stream_events':chunks,'client_seconds':elapsed,'first_content_seconds':first,'error':error,'metrics':final,'memory_sampling_interval_seconds':.25,'sampled_peak_rss_sum_bytes':max((s['rss_bytes_sum'] for s in samples),default=0),'sampled_peak_private_sum_bytes':max((s['private_bytes_sum'] for s in samples),default=0),'memory_sample_count':len(samples)} if final and final.get('eval_duration'): result['generation_tokens_per_second']=final['eval_count']/(final['eval_duration']/1e9) save(f'{task_id}-{run}.json',result) try: result['model_placement']=api('/api/ps',timeout=15) except Exception as exc: result['placement_error']=type(exc).__name__+': '+str(exc) save(f'{task_id}-{run}.json',result) status('request-complete',task=task_id,run=run,seconds=round(elapsed,2),error=error) return result def main(): assert EXE.is_file(), 'Runtime not ready' assert not list(OUT.glob('smoke-*.json')) and not list(OUT.glob('extraction-[123].json')), 'Existing first-run evidence: create a separate labeled attempt rather than overwrite' sock=socket.socket() assert sock.connect_ex(('127.0.0.1',11434))!=0, 'Existing listener: do not disturb it' sock.close() save('fixtures.json',FIXTURE) save('fixture-freeze.json',{'utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'sha256':hashlib.sha256((LAB/'fixtures.json').read_bytes()).hexdigest()}) log=(PRIVATE/'server.log').open('wb') server=subprocess.Popen([str(EXE),'serve'],env=ENV,stdout=log,stderr=log,creationflags=subprocess.CREATE_NO_WINDOW) (PRIVATE/'server-pid.json').write_text(json.dumps({'pid':server.pid}),encoding='utf-8') try: for attempt in range(30): if server.poll() is not None: raise RuntimeError('Server exited; inspect private log') try: version=api('/api/version',timeout=2); break except Exception: time.sleep(1) else: raise RuntimeError('Server did not become ready') save('configuration.json',{'version':version,'model':MODEL,'cloud_disabled':True,'host':'127.0.0.1:11434','vulkan_disabled':True,'parallel_requests':1,'options':FIXTURE['options'],'psutil_version':psutil.__version__,'date':datetime.date.today().isoformat()}) status('model-download-started',model=MODEL) req=urllib.request.Request(BASE+'/api/pull',data=json.dumps({'model':MODEL,'stream':True}).encode(),headers={'Content-Type':'application/json'}) last=time.monotonic(); pull_events=[] with LOCAL_HTTP.open(req,timeout=300) as response: for line in response: event=json.loads(line) if event.get('error'): raise RuntimeError(event['error']) if time.monotonic()-last>20 or event.get('status')=='success': status('model-download',**event); last=time.monotonic() pull_events.append(event) save('pull-events.json',pull_events) show=api('/api/show',{'model':MODEL}) save('model-metadata.json',{k:show.get(k) for k in ('details','model_info','parameters','capabilities','modified_at')}) save('model-list.json',api('/api/tags')) manifests=list((LAB/'models'/'manifests').rglob('9b')) assert len(manifests)==1 manifest=manifests[0].read_bytes() save('manifest.json',json.loads(manifest)) save('model-download-record.json',{'manifest_sha256':hashlib.sha256(manifest).hexdigest(),'model_files_bytes':sum(p.stat().st_size for p in (LAB/'models').rglob('*') if p.is_file())}) for n in range(2): smoke=generate(server,'smoke','Reply with exactly: Local model ready.',n+1,128) if smoke['error'] or (n==1 and smoke['client_seconds']>90): status('diagnosis-required',reason='Smoke error or predeclared latency threshold'); return save('placement-cli.json',{'command':'ollama ps','stdout':cli('ps').stdout}) demo=subprocess.run(['powershell.exe','-NoProfile','-NonInteractive','-File',str(LAB/'first-answer.ps1')],env=ENV,capture_output=True,text=True,timeout=185,creationflags=subprocess.CREATE_NO_WINDOW) save('powershell-demo.json',{'exit_code':demo.returncode,'stdout':demo.stdout,'stderr':demo.stderr,'note':'Separate nonstreaming walkthrough check, excluded from timing comparisons.'}) if demo.returncode: status('walkthrough-command-failed',reason='PowerShell demo needs diagnosis before publication') for task in FIXTURE['tasks']: api('/api/generate',{'model':MODEL,'keep_alive':0}) unloaded=api('/api/ps') save(task['id']+'-before-cold.json',unloaded) assert not unloaded.get('models'), 'Model not unloaded' for n in range(3): result=generate(server,task['id'],task['prompt'],n+1) if result['error']: status('diagnosis-required',reason='Task request failed'); return status('tests-complete',streamed_requests=11,powershell_walkthrough_attempts=1) finally: try: owned_children=psutil.Process(server.pid).children(recursive=True) except psutil.NoSuchProcess: owned_children=[] try: api('/api/generate',{'model':MODEL,'keep_alive':0},timeout=15) except Exception: pass server.terminate() try: server.wait(timeout=10) except subprocess.TimeoutExpired: server.kill(); server.wait(timeout=5) for child in owned_children: try: if child.is_running(): child.terminate() except psutil.NoSuchProcess: pass _, alive=psutil.wait_procs(owned_children,timeout=5) for child in alive: try: child.kill() except psutil.NoSuchProcess: pass save('cleanup.json',{'server_stopped':server.poll() is not None,'owned_children_stopped':all(not p.is_running() for p in owned_children)}) log.close() if __name__=='__main__': main() ===== FILE: mechanical-checks.py.txt ===== """Score frozen mechanical criteria; summary meaning needs separate review.""" from pathlib import Path import csv, json, statistics ROOT=Path(__file__).resolve().parents[2] OUT=ROOT/'outputs/qwen-local-test' fixtures=json.loads((OUT/'fixtures.json').read_text(encoding='utf-8')) def unique_object(pairs): result={} for key,value in pairs: if key in result:raise ValueError('duplicate JSON key') result[key]=value return result def assess(task,output): if task['id']=='extraction': try: value=json.loads(output,object_pairs_hook=unique_object) assert isinstance(value,dict) and set(value)==set(task['expected']) assert value==task['expected'] assert all(isinstance(value[k],str) for k in ('event','date','time','city')) assert all(type(value[k]) in (int,float) for k in ('capacity','ticket_gbp')) assert value['contact_email'] is None return {'automatic':'pass','detail':'Exact keys, values and required types; valid JSON without duplicate keys.'} except (ValueError,AssertionError): return {'automatic':'fail','detail':'Does not satisfy exact JSON extraction criteria.'} if task['id']=='arithmetic': passed=output.strip()==task['expected'] return {'automatic':'pass' if passed else 'fail','detail':'Trimmed response must equal 176.'} lines=output.strip().splitlines() structure=len(lines)==3 and all(x.startswith('- ') for x in lines) counts=[len(x[2:].split()) for x in lines] if structure else [] return {'automatic':'semantic-review-required' if structure and all(n<=25 for n in counts) else 'fail','bullet_word_counts':counts,'detail':'Meaning, complete fact coverage and no added facts require independent semantic review.'} rows=[];records=[] for task in fixtures['tasks']: for run in range(1,4): data=json.loads((OUT/f"{task['id']}-{run}.json").read_text(encoding='utf-8')) assert data['request']['prompt']==task['prompt'] and data['request']['options']==fixtures['options'] assert data['request']['think'] is False score=assess(task,data['output']) metrics=data.get('metrics') or {} record={'task':task['id'],'run':run,'answer_check':score,'transport_error':data['error'],'completion_reason':metrics.get('done_reason'),'reported_done':metrics.get('done'),'output_tokens':metrics.get('eval_count'),'output_limit_respected':isinstance(metrics.get('eval_count'),int) and metrics['eval_count']<=256,'memory_samples':data['memory_sample_count']} records.append(record) rows.append({'task':task['id'],'run':run,'model_state':'unloaded' if run==1 else 'resident','first_content_seconds':data['first_content_seconds'],'client_seconds':data['client_seconds'],'load_seconds':metrics.get('load_duration',0)/1e9,'prompt_tokens':metrics.get('prompt_eval_count'),'output_tokens':metrics.get('eval_count'),'generation_tokens_per_second':data.get('generation_tokens_per_second'),'sampled_peak_working_set_sum_gib':data['sampled_peak_rss_sum_bytes']/1024**3,'completion_reason':metrics.get('done_reason'),'automatic_answer_check':score['automatic']}) with (OUT/'request-metrics.csv').open('w',newline='',encoding='utf-8') as f: writer=csv.DictWriter(f,fieldnames=list(rows[0]));writer.writeheader();writer.writerows(rows) (OUT/'mechanical-review.json').write_text(json.dumps(records,indent=2),encoding='utf-8') groups=[] for task in fixtures['tasks']: subset=[r for r in rows if r['task']==task['id']] groups.append({'task':task['id'],'unloaded_seconds':subset[0]['client_seconds'],'resident_seconds_range':[min(r['client_seconds'] for r in subset[1:]),max(r['client_seconds'] for r in subset[1:])],'generation_tps_range':[min(r['generation_tokens_per_second'] or 0 for r in subset),max(r['generation_tokens_per_second'] or 0 for r in subset)],'peak_sampled_working_set_sum_gib':max(r['sampled_peak_working_set_sum_gib'] for r in subset)}) (OUT/'timing-summary.json').write_text(json.dumps(groups,indent=2),encoding='utf-8') print(json.dumps({'mechanical_checks':records,'timing_summary':groups},indent=2)) ===== FILE: mechanical-review.json ===== [ { "task": "extraction", "run": 1, "answer_check": { "automatic": "pass", "detail": "Exact keys, values and required types; valid JSON without duplicate keys." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 80, "output_limit_respected": true, "memory_samples": 87 }, { "task": "extraction", "run": 2, "answer_check": { "automatic": "pass", "detail": "Exact keys, values and required types; valid JSON without duplicate keys." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 80, "output_limit_respected": true, "memory_samples": 59 }, { "task": "extraction", "run": 3, "answer_check": { "automatic": "pass", "detail": "Exact keys, values and required types; valid JSON without duplicate keys." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 80, "output_limit_respected": true, "memory_samples": 59 }, { "task": "summary", "run": 1, "answer_check": { "automatic": "semantic-review-required", "bullet_word_counts": [ 14, 11, 17 ], "detail": "Meaning, complete fact coverage and no added facts require independent semantic review." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 74, "output_limit_respected": true, "memory_samples": 86 }, { "task": "summary", "run": 2, "answer_check": { "automatic": "semantic-review-required", "bullet_word_counts": [ 14, 11, 17 ], "detail": "Meaning, complete fact coverage and no added facts require independent semantic review." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 74, "output_limit_respected": true, "memory_samples": 54 }, { "task": "summary", "run": 3, "answer_check": { "automatic": "semantic-review-required", "bullet_word_counts": [ 14, 11, 17 ], "detail": "Meaning, complete fact coverage and no added facts require independent semantic review." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 74, "output_limit_respected": true, "memory_samples": 54 }, { "task": "arithmetic", "run": 1, "answer_check": { "automatic": "fail", "detail": "Trimmed response must equal 176." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 3, "output_limit_respected": true, "memory_samples": 29 }, { "task": "arithmetic", "run": 2, "answer_check": { "automatic": "fail", "detail": "Trimmed response must equal 176." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 3, "output_limit_respected": true, "memory_samples": 3 }, { "task": "arithmetic", "run": 3, "answer_check": { "automatic": "fail", "detail": "Trimmed response must equal 176." }, "transport_error": null, "completion_reason": "stop", "reported_done": true, "output_tokens": 3, "output_limit_respected": true, "memory_samples": 3 } ] ===== FILE: model-download-record.json ===== { "manifest_sha256": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "model_files_bytes": 6594485878 } ===== FILE: model-list.json ===== { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "modified_at": "2026-09-21T22:52:51.1178819+03:00", "size": 6594474711, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M", "context_length": 262144, "embedding_length": 4096 }, "capabilities": [ "completion", "vision", "tools", "thinking" ] } ] } ===== FILE: model-metadata.json ===== { "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "model_info": { "general.architecture": "qwen35", "general.file_type": 15, "general.parameter_count": 9653104368, "general.quantization_version": 2, "qwen35.attention.head_count": 16, "qwen35.attention.head_count_kv": [ 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 4 ], "qwen35.attention.key_length": 256, "qwen35.attention.layer_norm_rms_epsilon": 1e-06, "qwen35.attention.value_length": 256, "qwen35.block_count": 32, "qwen35.context_length": 262144, "qwen35.embedding_length": 4096, "qwen35.feed_forward_length": 12288, "qwen35.full_attention_interval": 4, "qwen35.image_token_id": 248056, "qwen35.mrope_sections": [ 11, 11, 10 ], "qwen35.rope.dimension_count": 64, "qwen35.rope.dimension_sections": [ 11, 11, 10 ], "qwen35.rope.freq_base": 10000000, "qwen35.rope.mrope_interleaved": true, "qwen35.rope.mrope_section": [ 11, 11, 10 ], "qwen35.ssm.conv_kernel": 4, "qwen35.ssm.group_count": 16, "qwen35.ssm.inner_size": 4096, "qwen35.ssm.state_size": 128, "qwen35.ssm.time_step_rank": 32, "qwen35.ssm.v_head_reordered": true, "qwen35.vision.attention.head_count": 16, "qwen35.vision.block_count": 27, "qwen35.vision.deepstack_visual_indexes": [], "qwen35.vision.embedding_length": 1152, "qwen35.vision.image_mean": [ 0.5, 0.5, 0.5 ], "qwen35.vision.image_std": [ 0.5, 0.5, 0.5 ], "qwen35.vision.longest_edge": 16777216, "qwen35.vision.num_channels": 3, "qwen35.vision.patch_size": 16, "qwen35.vision.shortest_edge": 65536, "qwen35.vision.spatial_merge_size": 2, "qwen35.vision.temporal_patch_size": 2, "qwen35.vision_end_token_id": 248054, "qwen35.vision_start_token_id": 248053, "tokenizer.ggml.add_eos_token": false, "tokenizer.ggml.add_padding_token": false, "tokenizer.ggml.eos_token_id": 248046, "tokenizer.ggml.merges": [], "tokenizer.ggml.model": "gpt2", "tokenizer.ggml.padding_token_id": 248044, "tokenizer.ggml.pre": "qwen35", "tokenizer.ggml.scores": [], "tokenizer.ggml.token_type": [], "tokenizer.ggml.tokens": [] }, "parameters": "top_k 20\ntop_p 0.95\npresence_penalty 1.5\ntemperature 1", "capabilities": [ "completion", "vision", "tools", "thinking" ], "modified_at": "2026-09-21T22:52:51.1178819+03:00" } ===== FILE: placement-cli.json ===== { "command": "ollama ps", "stdout": "NAME ID SIZE PROCESSOR CONTEXT UNTIL \nqwen3.5:9b 6488c96fa5fa 6.1 GB 100% CPU 4096 4 minutes from now \n" } ===== FILE: powershell-command-demo.json ===== { "delivery": "Individual PowerShell commands, not a script-file launch", "command": "$request = @{\n model = 'qwen3.5:9b'\n prompt = 'Reply with exactly: Local model ready.'\n think = $false\n stream = $false\n keep_alive = '5m'\n options = @{\n num_ctx = 4096\n num_predict = 128\n num_thread = 8\n temperature = 0\n seed = 42\n }\n} | ConvertTo-Json -Depth 5\n\n$answer = Invoke-RestMethod -Uri 'http://127.0.0.1:11434/api/generate' `\n -Method Post -ContentType 'application/json' -Body $request -TimeoutSec 180\n$answer.response\n", "exit_code": 0, "stdout": "Local model ready.\n", "stderr": "", "execution_policy_before": "", "execution_policy_after": "", "note": "Separate nonstreaming walkthrough check; excluded from the nine-request timing comparisons. The original blocked script-file attempt is preserved separately." } ===== FILE: powershell-script-file-attempt.json ===== { "exit_code": 1, "stdout": "", "stderr": "File [project]/first-answer.ps1 cannot be loaded because running \nscripts is disabled on this system. For more information, see about_Execution_Policies at \nhttps:/go.microsoft.com/fwlink/?LinkID=135170.\n + CategoryInfo : SecurityError: (:) [], ParentContainsErrorRecordException\n + FullyQualifiedErrorId : UnauthorizedAccess\n", "note": "Separate nonstreaming walkthrough check, excluded from timing comparisons.", "redaction_note": "Only the private local script path is redacted; the original record is retained privately." } ===== FILE: request-metrics.csv ===== task,run,model_state,first_content_seconds,client_seconds,load_seconds,prompt_tokens,output_tokens,generation_tokens_per_second,sampled_peak_working_set_sum_gib,completion_reason,automatic_answer_check extraction,1,unloaded,7.860895800000435,23.999580400002742,5.5831395,149,80,4.951407812120811,6.553009033203125,stop,pass extraction,2,resident,0.2559559999972407,16.36243319999994,0.0038427,149,80,4.969470986768597,6.553230285644531,stop,pass extraction,3,resident,0.27480450000075507,16.539104499999667,0.0025827,149,80,4.9193615185281905,6.553440093994141,stop,pass summary,1,unloaded,8.721332100001746,23.759764400001586,5.8416453,142,74,4.921558668271449,6.552692413330078,stop,semantic-review-required summary,2,resident,0.2695491000013135,15.140355499999714,0.0035964,142,74,4.9768770258075286,6.552860260009766,stop,semantic-review-required summary,3,resident,0.2588818000003812,15.110820300000341,0.0037116,142,74,4.983328073901948,6.552925109863281,stop,semantic-review-required arithmetic,1,unloaded,7.1968592000011995,7.601370800002769,5.5957209,73,3,7.460292592675485,6.532695770263672,stop,fail arithmetic,2,resident,0.30650239999886253,0.7749631000006048,0.0027886,73,3,6.428002091243346,6.532772064208984,stop,fail arithmetic,3,resident,0.28059159999975236,0.6767712999971991,0.0034883,73,3,7.518118665985024,6.532562255859375,stop,fail ===== FILE: runtime.json ===== { "tag_name": "v0.34.2", "published_at": "2026-09-15T21:21:07Z", "html_url": "https://github.com/ollama/ollama/releases/tag/v0.34.2", "asset": { "name": "ollama-windows-amd64.zip", "size": 1460928014, "browser_download_url": "https://github.com/ollama/ollama/releases/download/v0.34.2/ollama-windows-amd64.zip", "digest": "sha256:8f3fd071a2a2f9497b562f43502c77c2b701a99d1ee5dfda28da8c786373063b" }, "download_seconds_resumed": 1068.55, "sha256_verified": "8f3fd071a2a2f9497b562f43502c77c2b701a99d1ee5dfda28da8c786373063b", "uncompressed_bytes": 1928951606 } ===== FILE: smoke-1.json ===== { "task": "smoke", "run": 1, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Reply with exactly: Local model ready.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 128, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "Local model ready.", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:57.3275694Z", "response": "Local", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:57.5341427Z", "response": " model", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:57.7399472Z", "response": " ready", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:57.9518168Z", "response": ".", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:58.166494Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 20206, 440, 6681, 25, 8509, 1558, 5354, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 7090, 1558, 5354, 13 ], "total_duration": 6933194800, "load_duration": 5583436200, "prompt_eval_count": 20, "prompt_eval_cached_count": 0, "prompt_eval_duration": 506780000, "eval_count": 5, "eval_duration": 839053000 } ], "client_seconds": 6.948197499998059, "first_content_seconds": 6.108000099997298, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:58.166494Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 20206, 440, 6681, 25, 8509, 1558, 5354, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 7090, 1558, 5354, 13 ], "total_duration": 6933194800, "load_duration": 5583436200, "prompt_eval_count": 20, "prompt_eval_cached_count": 0, "prompt_eval_duration": 506780000, "eval_count": 5, "eval_duration": 839053000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 6999957504, "sampled_peak_private_sum_bytes": 7986786304, "memory_sample_count": 26, "generation_tokens_per_second": 5.959099127230341, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:57:58.1681007+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: smoke-2.json ===== { "task": "smoke", "run": 2, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Reply with exactly: Local model ready.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 128, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "Local model ready.", "stream_events": [ { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:58.4563558Z", "response": "Local", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:58.666192Z", "response": " model", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:58.876051Z", "response": " ready", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:59.0840657Z", "response": ".", "done": false }, { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:59.2998433Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 20206, 440, 6681, 25, 8509, 1558, 5354, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 7090, 1558, 5354, 13 ], "total_duration": 1097237000, "load_duration": 3685300, "prompt_eval_count": 20, "prompt_eval_cached_count": 16, "prompt_eval_duration": 245342000, "eval_count": 5, "eval_duration": 843102000 } ], "client_seconds": 1.1130603000019619, "first_content_seconds": 0.2684437000025355, "error": null, "metrics": { "model": "qwen3.5:9b", "created_at": "2026-09-21T19:52:59.2998433Z", "response": "", "done": true, "done_reason": "stop", "context": [ 248045, 846, 198, 20206, 440, 6681, 25, 8509, 1558, 5354, 13, 248046, 198, 248045, 74455, 198, 248068, 271, 248069, 271, 7090, 1558, 5354, 13 ], "total_duration": 1097237000, "load_duration": 3685300, "prompt_eval_count": 20, "prompt_eval_cached_count": 16, "prompt_eval_duration": 245342000, "eval_count": 5, "eval_duration": 843102000 }, "memory_sampling_interval_seconds": 0.25, "sampled_peak_rss_sum_bytes": 7000215552, "sampled_peak_private_sum_bytes": 7986929664, "memory_sample_count": 4, "generation_tokens_per_second": 5.930480534976788, "model_placement": { "models": [ { "name": "qwen3.5:9b", "model": "qwen3.5:9b", "size": 6071601067, "digest": "6488c96fa5faab64bb65cbd30d4289e20e6130ef535a93ef9a49f42eda893ea7", "details": { "parent_model": "", "format": "gguf", "family": "qwen35", "families": [ "qwen35" ], "parameter_size": "9.7B", "quantization_level": "Q4_K_M" }, "expires_at": "2026-09-21T22:57:59.3013797+03:00", "size_vram": 0, "context_length": 4096 } ] } } ===== FILE: summary-1.json ===== { "task": "summary", "run": 1, "model": "qwen3.5:9b", "request": { "model": "qwen3.5:9b", "prompt": "Summarize this fictional update in exactly three bullets, beginning each with \"- \". Use one bullet each for opening, completed work, and remaining restrictions. Each bullet must contain at most 25 words. Include the stated numbers and preserve uncertainty. Add no facts.\n\nRiverside Pocket Park opens on 4 October 2026, with daily opening hours of 07:00–21:00. Phase one added a 2 km walking path and 30 trees. The playground remains closed pending inspection; no reopening date has been announced. Drinking fountains are not installed, so visitors should bring water.", "stream": true, "think": false, "keep_alive": "5m", "options": { "num_ctx": 4096, "num_predict": 256, "temperature": 0, "seed": 42, "num_thread": 8 } }, "output": "- Riverside Pocket Park opens October 4, 2026, with daily hours from 07:00 to 21:00.\n- Phase one added a 2 km walking path and 30 trees.\n- The playground remains closed pending inspection; no reopening date has been announced. 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timing-summary.json ===== [ { "task": "extraction", "unloaded_seconds": 23.999580400002742, "resident_seconds_range": [ 16.36243319999994, 16.539104499999667 ], "generation_tps_range": [ 4.9193615185281905, 4.969470986768597 ], "peak_sampled_working_set_sum_gib": 6.553440093994141 }, { "task": "summary", "unloaded_seconds": 23.759764400001586, "resident_seconds_range": [ 15.110820300000341, 15.140355499999714 ], "generation_tps_range": [ 4.921558668271449, 4.983328073901948 ], "peak_sampled_working_set_sum_gib": 6.552925109863281 }, { "task": "arithmetic", "unloaded_seconds": 7.601370800002769, "resident_seconds_range": [ 0.6767712999971991, 0.7749631000006048 ], "generation_tps_range": [ 6.428002091243346, 7.518118665985024 ], "peak_sampled_working_set_sum_gib": 6.532772064208984 } ] ===== FILE: walkthrough-cli.json ===== [ { "command": "ollama --version", "exit_code": 0, "stdout": "ollama version is 0.34.2\n", "stderr": "" }, { "command": "ollama pull --help", "exit_code": 0, "stdout": "Pull a model from a registry\n\nUsage:\n ollama pull MODEL [flags]\n\nFlags:\n -h, --help help for pull\n --insecure Use an insecure registry\n\nEnvironment Variables:\n OLLAMA_HOST IP Address for the ollama server (default 127.0.0.1:11434)\n", "stderr": "" }, { "command": "ollama list", "exit_code": 0, "stdout": "NAME ID SIZE MODIFIED \nqwen3.5:9b 6488c96fa5fa 6.6 GB 5 minutes ago \n", "stderr": "" }, { "command": "ollama show qwen3.5:9b", "exit_code": 0, "stdout": " Model\n architecture qwen35 \n parameters 9.7B \n context length 262144 \n embedding length 4096 \n quantization Q4_K_M \n requires 0.17.1 \n\n Capabilities\n completion \n vision \n tools \n thinking \n\n Parameters\n presence_penalty 1.5 \n temperature 1 \n top_k 20 \n top_p 0.95 \n\n License\n Apache License \n Version 2.0, January 2004 \n ... \n\n", "stderr": "" }, { "command": "ollama show qwen3.5:9b --parameters", "exit_code": 0, "stdout": "presence_penalty 1.5\ntemperature 1\ntop_k 20\ntop_p 0.95\n", "stderr": "" } ] ===== FILE: walkthrough-stop-cli.json ===== [ { "command": "ollama ps", "exit_code": 0, "stdout": "NAME ID SIZE PROCESSOR CONTEXT UNTIL \nqwen3.5:9b 6488c96fa5fa 6.1 GB 100% CPU 4096 4 minutes from now \n", "stderr": "" }, { "command": "ollama stop qwen3.5:9b", "exit_code": 0, "stdout": "", "stderr": "\u001b[?2026h\u001b[?25l\u001b[1G\u001b[K\u001b[?25h\u001b[?2026l\u001b[?25l\u001b[2K\u001b[1G\u001b[?25h" }, { "command": "ollama ps", "exit_code": 0, "stdout": "NAME ID SIZE PROCESSOR CONTEXT UNTIL \n", "stderr": "" } ] ===== FILE: walkthrough-unloaded.json ===== { "models": [] }