ChinaAI DeepSeek local Windows experiment — complete public record


===== README.md =====
# ChinaAI DeepSeek local Windows records

Created 2026-09-22T22:37:36.321313+00:00. These are the original first response records from one Windows/Ollama experiment, plus the frozen criteria and a separate editorial assessment. The extraction, summary and arithmetic files retain complete Ollama stream events, thinking and final response separately. Only final response is scored. No model binary, local profile, server log, API key or account information is included. The exact prompts are fictional.

The official Ollama package, DeepSeek upstream model card and source-review notes are linked from the public article. The first model download attempt was blocked by the command environment's network sandbox before inference. Network access was then approved and the same frozen fixture was used; no generation occurred before the successful pull.

Limit: three different first requests are examples, not a reliability percentage or a matched comparison. A 250ms working-set sample can miss peaks and count shared pages more than once. The installed RAM is a characteristic of this machine, not a requirement for yours. Reproduce with the same package manifest, runtime and settings, and expect hardware and package differences to affect outcomes.


===== fixtures.json =====
{
  "frozen_before_inference": "2026-09-23",
  "scope": "Three first, stateless, text-only requests for this exact DeepSeek distill on one Windows machine. No retry, warm-up request, broad benchmark or Qwen comparison.",
  "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
  "think": true,
  "options": {"num_ctx": 4096, "num_predict": 2048, "num_thread": 8, "temperature": 0.6, "top_p": 0.95, "seed": 42},
  "sampling_note": "Eight CPU threads limit impact on the active desktop rather than maximize throughput. A requested setting alone does not prove actual CPU placement.",
  "run_order": "Three different prompts in order. First request begins with the model unloaded; later requests may use a resident model. The operating-system file cache is not cleared. No time comparison is inferred from these different prompts.",
  "timeout_seconds": 600,
  "scoring": "Evaluate final response only. Preserve thinking separately. Empty, truncated or transport-failed first attempts are failures and must remain in the record.",
  "tasks": [
    {
      "id": "extraction",
      "prompt": "Return only one JSON object with exactly these keys: order_id, customer, item, quantity, unit_price, currency, delivery_date. Use JSON numbers for quantity and unit_price. Use null for an absent delivery date. Source: Order CED-217 was placed by Mira Hassan for 6 notebooks at GBP 2.75 each. No delivery date is stated.",
      "expected": {"order_id": "CED-217", "customer": "Mira Hassan", "item": "notebooks", "quantity": 6, "unit_price": 2.75, "currency": "GBP", "delivery_date": null},
      "pass_criteria": "One strict JSON object with exact keys, types and values, no code fences or surrounding prose. Key order irrelevant."
    },
    {
      "id": "summary",
      "prompt": "Summarize the source in exactly three bullets and at most 70 words. Preserve every stated practical detail; add nothing. Source: The Saturday workshop is at Cedar Hall, from 10:00 to 11:30. It is for adults and entry is free. Bring a refillable water bottle. Use the side entrance because the front steps are under repair.",
      "pass_criteria": "Exactly three bullets and no more than 70 whitespace-separated words; Saturday, Cedar Hall, 10:00–11:30, adults, free, refillable bottle, side entrance and repair reason all present; no invented facts. Semantic review required."
    },
    {
      "id": "arithmetic",
      "prompt": "Calculate the final amount in GBP. Confirmed items: 8 badges at GBP 7.50 each and 3 folders at GBP 12.00 each. Cancelled items: 2 posters at GBP 15.00 each; exclude them. Apply a 10% discount to the confirmed-item subtotal, then add GBP 4.50 delivery. Give the final amount and one compact calculation, in no more than two sentences.",
      "expected_final_gbp": 90.90,
      "pass_criteria": "Final GBP 90.90, confirmed subtotal 96.00, discount 9.60, discounted subtotal 86.40, cancelled posters excluded, delivery added after discount, no more than two sentences."
    }
  ]
}


===== protocol-details.md =====
# Protocol amendment before first inference

The initial fixture was saved before the first model-pull request. That request was blocked by the command environment before the manifest downloaded; there was no inference. Before any generation, the runner was amended to record time to first final-answer text, `ollama ps` placement after each task, and an empty loaded-model check after unloading. The three prompts, checks, order, exact model tag, thinking setting, 4,096 context, 2,048 generated-token cap, 8 threads, temperature 0.6, top-p 0.95, seed 42 and 600-second deadline did not change.

The final runner makes streamed local `/api/generate` requests (`stream=true`, `think=true`) with a five-minute model residency after each answer (`keep_alive='5m'`); it stores each stream event unedited and separates `thinking` from final `response`. The generated-token allowance covers both. The first model request starts after a completed official manifest pull, without a warm-up generation. The later two requests use different prompts and may benefit from an already resident model. There are exactly three scored first requests; a separately tested reader-facing command, if any, must be disclosed as an extra request.

The model transfer was changed to checkpointed 32 MiB HTTP Range segments after Ollama's large parallel parts repeatedly lost partial progress when the network interrupted. The assembled blob is accepted only if its full SHA-256 matches the official registry digest. This is a setup intervention before inference, not an answer retry. The previously attempted pull and its network failure remain recorded separately.


===== public-host.json =====
{
  "utc": "2026-09-22T22:00:38.472267+00:00",
  "windows": [
    "11",
    "10.0.26200",
    "SP0",
    "Multiprocessor Free"
  ],
  "system": "Windows",
  "release": "11",
  "version": "10.0.26200",
  "logical_cpu_count": 28,
  "installed_ram_bytes_reported_by_psutil": 110852902912,
  "processor": "Intel Core i7-14700 (recorded in earlier local experiment)"
}

===== fixture-freeze.json =====
{
  "utc": "2026-09-22T21:14:51.835634+00:00",
  "sha256": "172c1505bd545190996d6c8c186c0abaebf27579708dadc246a5334284d0120a",
  "fixture": {
    "frozen_before_inference": "2026-09-23",
    "scope": "Three first, stateless, text-only requests for this exact DeepSeek distill on one Windows machine. No retry, warm-up request, broad benchmark or Qwen comparison.",
    "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
    "think": true,
    "options": {
      "num_ctx": 4096,
      "num_predict": 2048,
      "num_thread": 8,
      "temperature": 0.6,
      "top_p": 0.95,
      "seed": 42
    },
    "sampling_note": "Eight CPU threads limit impact on the active desktop rather than maximize throughput. A requested setting alone does not prove actual CPU placement.",
    "run_order": "Three different prompts in order. First request begins with the model unloaded; later requests may use a resident model. The operating-system file cache is not cleared. No time comparison is inferred from these different prompts.",
    "timeout_seconds": 600,
    "scoring": "Evaluate final response only. Preserve thinking separately. Empty, truncated or transport-failed first attempts are failures and must remain in the record.",
    "tasks": [
      {
        "id": "extraction",
        "prompt": "Return only one JSON object with exactly these keys: order_id, customer, item, quantity, unit_price, currency, delivery_date. Use JSON numbers for quantity and unit_price. Use null for an absent delivery date. Source: Order CED-217 was placed by Mira Hassan for 6 notebooks at GBP 2.75 each. No delivery date is stated.",
        "expected": {
          "order_id": "CED-217",
          "customer": "Mira Hassan",
          "item": "notebooks",
          "quantity": 6,
          "unit_price": 2.75,
          "currency": "GBP",
          "delivery_date": null
        },
        "pass_criteria": "One strict JSON object with exact keys, types and values, no code fences or surrounding prose. Key order irrelevant."
      },
      {
        "id": "summary",
        "prompt": "Summarize the source in exactly three bullets and at most 70 words. Preserve every stated practical detail; add nothing. Source: The Saturday workshop is at Cedar Hall, from 10:00 to 11:30. It is for adults and entry is free. Bring a refillable water bottle. Use the side entrance because the front steps are under repair.",
        "pass_criteria": "Exactly three bullets and no more than 70 whitespace-separated words; Saturday, Cedar Hall, 10:00–11:30, adults, free, refillable bottle, side entrance and repair reason all present; no invented facts. Semantic review required."
      },
      {
        "id": "arithmetic",
        "prompt": "Calculate the final amount in GBP. Confirmed items: 8 badges at GBP 7.50 each and 3 folders at GBP 12.00 each. Cancelled items: 2 posters at GBP 15.00 each; exclude them. Apply a 10% discount to the confirmed-item subtotal, then add GBP 4.50 delivery. Give the final amount and one compact calculation, in no more than two sentences.",
        "expected_final_gbp": 90.9,
        "pass_criteria": "Final GBP 90.90, confirmed subtotal 96.00, discount 9.60, discounted subtotal 86.40, cancelled posters excluded, delivery added after discount, no more than two sentences."
      }
    ]
  }
}

===== protocol-hashes.json =====
{
  "protocol_note_sha256": "b4f6185964fb5bf67e2e597b2064aedc32b94914fa0dd1f2fb84e6898ac337f6",
  "fixtures_sha256": "172c1505bd545190996d6c8c186c0abaebf27579708dadc246a5334284d0120a",
  "runner_sha256": "2dc933481048c0bf7ad50ba7a75623e68ebdf4f8ee90f41443379d81d3f022e5",
  "utc": "2026-09-22T22:19:40.2635314Z"
}


===== runtime.json =====
{
  "version": {
    "version": "0.34.2"
  },
  "host": "127.0.0.1:11435",
  "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
  "cloud_disabled": true,
  "vulkan_disabled": true,
  "psutil_version": "7.2.2"
}

===== manifest.json =====
{
  "schemaVersion": 2,
  "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
  "config": {
    "mediaType": "application/vnd.docker.container.image.v1+json",
    "digest": "sha256:f64cd5418e4b038ef90cf5fab6eb7ce6ae8f18909416822751d3b9fca827c2ab",
    "size": 487
  },
  "layers": [
    {
      "mediaType": "application/vnd.ollama.image.model",
      "digest": "sha256:e6a7edc1a4d7d9b2de136a221a57336b76316cfe53a252aeba814496c5ae439d",
      "size": 5225373760
    },
    {
      "mediaType": "application/vnd.ollama.image.template",
      "digest": "sha256:c5ad996bda6eed4df6e3b605a9869647624851ac248209d22fd5e2c0cc1121d3",
      "size": 556
    },
    {
      "mediaType": "application/vnd.ollama.image.license",
      "digest": "sha256:6e4c38e1172f42fdbff13edf9a7a017679fb82b0fde415a3e8b3c31c6ed4a4e4",
      "size": 1065
    },
    {
      "mediaType": "application/vnd.ollama.image.params",
      "digest": "sha256:ed8474dc73db8ca0d85c1958c91c3a444e13a469c2efb10cd777ca9baeaddcb7",
      "size": 179
    }
  ]
}

===== download-record.json =====
{
  "manifest_sha256": "6995872bfe4c521a67b32da386cd21d5c6e819b6e0d62f79f64ec83be99f5763",
  "stored_file_bytes": 5225381416
}

===== model-show.json =====
{
  "license": "MIT License\n\nCopyright (c) 2023 DeepSeek\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n",
  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this, replace FROM with:\n# FROM deepseek-r1:8b-0528-qwen3-q4_K_M\n\nFROM C:\\Users\\HP\\Documents\\Codex\\2026-09-18\\fr\\work\\deepseek-local-lab\\models\\blobs\\sha256-e6a7edc1a4d7d9b2de136a221a57336b76316cfe53a252aeba814496c5ae439d\nTEMPLATE \"\"\"{{- if .System }}{{ .System }}{{ end }}\n{{- range $i, $_ := .Messages }}\n{{- $last := eq (len (slice $.Messages $i)) 1}}\n{{- if eq .Role \"user\" }}<｜User｜>{{ .Content }}\n{{- else if eq .Role \"assistant\" }}<｜Assistant｜>\n  {{- if and $.IsThinkSet (and $last .Thinking) -}}\n<think>\n{{ .Thinking }}\n</think>\n{{- end }}{{ .Content }}{{- if not $last }}<｜end▁of▁sentence｜>{{- end }}\n{{- end }}\n{{- if and $last (ne .Role \"assistant\") }}<｜Assistant｜>\n{{- if and $.IsThinkSet (not $.Think) -}}\n<think>\n\n</think>\n\n{{ end }}\n{{- end -}}\n{{- end }}\"\"\"\nPARAMETER temperature 0.6\nPARAMETER top_p 0.95\nPARAMETER stop <｜begin▁of▁sentence｜>\nPARAMETER stop <｜end▁of▁sentence｜>\nPARAMETER stop <｜User｜>\nPARAMETER stop <｜Assistant｜>\nLICENSE \"\"\"MIT License\n\nCopyright (c) 2023 DeepSeek\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n",
  "parameters": "stop                           \"<｜begin▁of▁sentence｜>\"\nstop                           \"<｜end▁of▁sentence｜>\"\nstop                           \"<｜User｜>\"\nstop                           \"<｜Assistant｜>\"\ntemperature                    0.6\ntop_p                          0.95",
  "template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<｜User｜>' + content + '<｜Assistant｜>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{content + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + content + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\n<｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}",
  "details": {
    "parent_model": "",
    "format": "gguf",
    "family": "qwen3",
    "families": [
      "qwen3"
    ],
    "parameter_size": "8.2B",
    "quantization_level": "Q4_K_M"
  },
  "model_info": {
    "general.architecture": "qwen3",
    "general.basename": "DeepSeek-R1-0528-Qwen3",
    "general.file_type": 15,
    "general.license": "mit",
    "general.parameter_count": 8190735360,
    "general.quantization_version": 2,
    "general.size_label": "8B",
    "general.type": "model",
    "qwen3.attention.head_count": 32,
    "qwen3.attention.head_count_kv": 8,
    "qwen3.attention.key_length": 128,
    "qwen3.attention.layer_norm_rms_epsilon": 1e-06,
    "qwen3.attention.value_length": 128,
    "qwen3.block_count": 36,
    "qwen3.context_length": 131072,
    "qwen3.embedding_length": 4096,
    "qwen3.feed_forward_length": 12288,
    "qwen3.rope.freq_base": 1000000,
    "qwen3.rope.scaling.factor": 4,
    "qwen3.rope.scaling.original_context_length": 32768,
    "qwen3.rope.scaling.type": "yarn",
    "tokenizer.ggml.add_bos_token": false,
    "tokenizer.ggml.add_eos_token": false,
    "tokenizer.ggml.bos_token_id": 151643,
    "tokenizer.ggml.eos_token_id": 151645,
    "tokenizer.ggml.merges": [],
    "tokenizer.ggml.model": "gpt2",
    "tokenizer.ggml.padding_token_id": 151645,
    "tokenizer.ggml.pre": "qwen2",
    "tokenizer.ggml.token_type": [],
    "tokenizer.ggml.tokens": []
  },
  "capabilities": [
    "tools",
    "thinking",
    "completion"
  ],
  "modified_at": "2026-09-23T01:27:55.6690073+03:00"
}

===== model-tags.json =====
{
  "models": [
    {
      "name": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "modified_at": "2026-09-23T01:27:55.6690073+03:00",
      "size": 5225376047,
      "digest": "6995872bfe4c521a67b32da386cd21d5c6e819b6e0d62f79f64ec83be99f5763",
      "details": {
        "parent_model": "",
        "format": "gguf",
        "family": "qwen3",
        "families": [
          "qwen3"
        ],
        "parameter_size": "8.2B",
        "quantization_level": "Q4_K_M",
        "context_length": 131072,
        "embedding_length": 4096
      },
      "capabilities": [
        "tools",
        "thinking",
        "completion"
      ]
    }
  ]
}

===== extraction.json =====
{
  "task": {
    "id": "extraction",
    "prompt": "Return only one JSON object with exactly these keys: order_id, customer, item, quantity, unit_price, currency, delivery_date. Use JSON numbers for quantity and unit_price. Use null for an absent delivery date. Source: Order CED-217 was placed by Mira Hassan for 6 notebooks at GBP 2.75 each. No delivery date is stated.",
    "expected": {
      "order_id": "CED-217",
      "customer": "Mira Hassan",
      "item": "notebooks",
      "quantity": 6,
      "unit_price": 2.75,
      "currency": "GBP",
      "delivery_date": null
    },
    "pass_criteria": "One strict JSON object with exact keys, types and values, no code fences or surrounding prose. Key order irrelevant."
  },
  "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
  "request": {
    "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
    "prompt": "Return only one JSON object with exactly these keys: order_id, customer, item, quantity, unit_price, currency, delivery_date. Use JSON numbers for quantity and unit_price. Use null for an absent delivery date. Source: Order CED-217 was placed by Mira Hassan for 6 notebooks at GBP 2.75 each. No delivery date is stated.",
    "stream": true,
    "think": true,
    "keep_alive": "5m",
    "options": {
      "num_ctx": 4096,
      "num_predict": 2048,
      "num_thread": 8,
      "temperature": 0.6,
      "top_p": 0.95,
      "seed": 42
    }
  },
  "thinking": "Okay, the user wants me to return a specific JSON object with exact keys: order_id, customer, item, quantity, unit_price, currency, and delivery_date. They've provided the source information - Order CED-217 placed by Mira Hassan for 6 notebooks at GBP 2.75 each, with no delivery date mentioned.\n\nI need to make sure my response follows their instructions precisely. They're asking for one JSON object only, with those exact keys, using JSON numbers for quantity and unit_price, and null for the delivery_date since it's absent. This seems like a straightforward data structuring task, probably for an API or database integration.\n\nThe user might be a developer, data scientist, or someone working with order management systems. They're likely building something where structured data is crucial, and they want to avoid any extra text or formatting issues. By specifying the exact keys and data types, they're emphasizing the need for clean, machine-readable output.\n\nI'll extract the details from the order description: order_id is CED-217, customer is Mira Hassan, item is notebooks, quantity is 6, unit_price is 2.75, currency is GBP, and delivery_date is null. I need to convert quantity and unit_price to JSON numbers, so I'll use integers or decimals as appropriate.\n\nI should ensure the JSON is valid - proper syntax, comma-separated values without trailing commas, and quotes around keys and strings. The delivery_date being absent means I'll use null, which is correct according to the instructions.\n\nThis response is efficient and directly addresses the user's need. It avoids any unnecessary explanation, sticking strictly to the JSON format they requested.\n",
  "response": "```json\n{\n  \"order_id\": \"CED-217\",\n  \"customer\": \"Mira Hassan\",\n  \"item\": \"notebooks\",\n  \"quantity\": 6,\n  \"unit_price\": 2.75,\n  \"currency\": \"GBP\",\n  \"delivery_date\": null\n}\n```",
  "stream_events": [
    {
      "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "created_at": "2026-09-22T22:28:03.8450793Z",
      "response": "",
      "done": false
    },
    {
      "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "created_at": "2026-09-22T22:28:04.0896319Z",
      "response": "",
      "done": false
    },
    {
      "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "created_at": "2026-09-22T22:28:04.3224918Z",
      "response": "",
      "thinking": "Okay",
      "done": false
    },
    {
      "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
      "created_at": "2026-09-22T22:28:04.5639464Z",
      "response": "",
      "thinking": ",",
      "done": false
    },
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===== summary.json =====
{
  "task": {
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    "prompt": "Summarize the source in exactly three bullets and at most 70 words. Preserve every stated practical detail; add nothing. Source: The Saturday workshop is at Cedar Hall, from 10:00 to 11:30. It is for adults and entry is free. Bring a refillable water bottle. Use the side entrance because the front steps are under repair.",
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===== arithmetic.json =====
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    "prompt": "Calculate the final amount in GBP. Confirmed items: 8 badges at GBP 7.50 each and 3 folders at GBP 12.00 each. Cancelled items: 2 posters at GBP 15.00 each; exclude them. Apply a 10% discount to the confirmed-item subtotal, then add GBP 4.50 delivery. Give the final amount and one compact calculation, in no more than two sentences.",
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    "prompt": "Calculate the final amount in GBP. Confirmed items: 8 badges at GBP 7.50 each and 3 folders at GBP 12.00 each. Cancelled items: 2 posters at GBP 15.00 each; exclude them. Apply a 10% discount to the confirmed-item subtotal, then add GBP 4.50 delivery. Give the final amount and one compact calculation, in no more than two sentences.",
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  "thinking": "First, the user wants the final amount in GBP. I have confirmed items: 8 badges at GBP 7.50 each and 3 folders at GBP 12.00 each. I need to calculate the subtotal for these.\n\nStart by finding the cost of badges: 8 badges * GBP 7.50 each. That's 8 * 7.50. 8 * 7 = 56, and 8 * 0.50 = 4, so 56 + 4 = GBP 60.00 for badges.\n\nNow, folders: 3 folders * GBP 12.00 each. 3 * 12 = GBP 36.00.\n\nAdd these to get the confirmed-item subtotal: GBP 60.00 + GBP 36.00 = GBP 96.00.\n\nThe instruction says to apply a 10% discount to the confirmed-item subtotal. So, 10% of GBP 96.00 is (10/100) * 96.00 = 0.10 * 96.00 = GBP 9.60.\n\nSubtract the discount from the subtotal: GBP 96.00 - GBP 9.60 = GBP 86.40. This is the discounted total.\n\nThen, add GBP 4.50 for delivery. So, GBP 86.40 + GBP 4.50.\n\nCalculate that: 86.40 + 4.50. 86 + 4 = 90, and 0.40 + 0.50 = 0.90, so GBP 90.90.\n\nThe user mentioned cancelled items: 2 posters at GBP 15.00 each, but it says to exclude them, so I don't include them in the calculation.\n\nNow, I need to provide the final amount and one compact calculation. The final amount is GBP 90.90.\n\nFor the compact calculation, I should summarize it in a simple way. Perhaps: (8 * 7.50 + 3 * 12.00) * 0.90 + 4.50 or something similar.\n\nLet me break it down step by step for clarity.\n\nFirst, confirmed items subtotal: (8 * 7.50) + (3 * 12.00) = 60.00 + 36.00 = 96.00\n\nThen, 10% discount: 96.00 * 0.90 = 86.40\n\nThen, add delivery: 86.40 + 4.50 = 90.90\n\nThe compact calculation could be: (8 * 7.50 + 3 * 12.00) * 0.90 + 4.50\n\nThat should be fine.\n\nThe user said \"in no more than two sentences,\" so I need to keep my response concise.\n\nFinal answer should be the amount, and I might need to box it as per the instruction.\n\nAlso, ensure the response is clear and direct.\n",
  "response": "The final amount is GBP 90.90.\n\nCompact calculation: (8 × £7.50 + 3 × £12.00) × 0.90 + £4.50 = £90.90",
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    "eval_duration": 221902944000
  },
  "error": null,
  "elapsed_seconds": 224.32314819999738,
  "first_text_seconds": 2.909239200002048,
  "first_final_text_seconds": 206.9218336000049,
  "sampled_peak_rss_sum_bytes": 6093447168,
  "sampled_peak_private_sum_bytes": 6267645952,
  "memory_sample_count": 745,
  "sample_interval_seconds": 0.25,
  "placement": {
    "models": [
      {
        "name": "deepseek-r1:8b-0528-qwen3-q4_K_M",
        "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
        "size": 5937267997,
        "digest": "6995872bfe4c521a67b32da386cd21d5c6e819b6e0d62f79f64ec83be99f5763",
        "details": {
          "parent_model": "",
          "format": "gguf",
          "family": "qwen3",
          "families": [
            "qwen3"
          ],
          "parameter_size": "8.2B",
          "quantization_level": "Q4_K_M"
        },
        "expires_at": "2026-09-23T01:40:06.9949937+03:00",
        "size_vram": 0,
        "context_length": 4096
      }
    ]
  },
  "ollama_ps": {
    "exit_code": 0,
    "stdout": "NAME                                ID              SIZE      PROCESSOR    CONTEXT    UNTIL              \ndeepseek-r1:8b-0528-qwen3-q4_K_M    6995872bfe4c    5.9 GB    100% CPU     4096       4 minutes from now    \n",
    "stderr": ""
  }
}

===== mechanical-review.json =====
{
  "reviewed_utc": "2026-09-22T22:35:57.954775+00:00",
  "method": "Frozen first final responses only; thinking is not scored. Mechanical checks do not replace semantic review.",
  "tasks": [
    {
      "id": "extraction",
      "transport_error": null,
      "done": true,
      "done_reason": "stop",
      "has_final_response": true,
      "checks": {
        "parses_as_json": false,
        "parse_error": "Expecting value: line 1 column 1 (char 0)"
      },
      "complete_under_cap": true
    },
    {
      "id": "summary",
      "transport_error": null,
      "done": true,
      "done_reason": "stop",
      "has_final_response": true,
      "checks": {
        "three_lines": true,
        "all_bullets": true,
        "word_count_excluding_markers": 29,
        "at_most_70_words": true,
        "semantic_facts_and_no_invention": "requires human-style editorial inspection of final text"
      },
      "complete_under_cap": true
    },
    {
      "id": "arithmetic",
      "transport_error": null,
      "done": true,
      "done_reason": "stop",
      "has_final_response": true,
      "checks": {
        "contains_gbp_90_90": true,
        "has_compact_calculation": "requires editorial inspection",
        "no_more_than_two_sentences": "requires editorial inspection",
        "cancelled_line_excluded": "requires editorial inspection"
      },
      "complete_under_cap": true
    }
  ]
}

===== editorial-assessment.md =====
# DeepSeek local Windows experiment: first-response assessment

Reviewed September 23, 2026. The three fictional prompts and exact pass criteria were frozen before inference. This assessment scores only each first final `response`, not the separately returned `thinking` field. No result was regenerated or edited. All three requests finished with `done_reason=stop`, without a transport error or output-cap truncation. This sample is not a reliability rate or a comparison across machines or models.

| Task | First-response finding | Fixed verdict |
| --- | --- | --- |
| Order JSON extraction | All seven requested fields had the correct values and types, but the response included a Markdown JSON fence. The complete string cannot be parsed as the requested raw JSON object. | Fail: exact output format. |
| Workshop summary | Three bullets and 29 words kept the useful logistical details. “For adults only” introduced an exclusivity restriction absent from the source's “for adults.” | Fail: no added facts. |
| Discounted total | The answer gave GBP 90.90 and the compact expression `(8 × £7.50 + 3 × £12.00) × 0.90 + £4.50 = £90.90` in two short sentences. Cancelled posters were not counted; the 10% discount was applied before delivery. | Pass. |

The model was the explicitly tagged `deepseek-r1:8b-0528-qwen3-q4_K_M`, manifest `6995872bfe4c521a67b32da386cd21d5c6e819b6e0d62f79f64ec83be99f5763`, served by Ollama 0.34.2 on a Windows 11 desktop. All three placements showed 100% CPU in Ollama's `ps` output, with 4,096 context tokens. Request elapsed times were 126.0, 80.5 and 224.3 seconds. First final-response text arrived after 105.1, 68.1 and 206.9 seconds, respectively. Thinking text began earlier. The largest sum of sampled owned-process working sets was 5.67 GiB; nominal sampling was every 250 ms and can miss peaks or double-count shared pages. This is not a memory requirement for another computer.

The raw records retain the exact prompts, full streams, final responses, timing and settings. A fourth, separately labelled PowerShell walkthrough request tests the public command and stop sequence; it is not included in the three scored results.


===== cleanup.json =====
{
  "server_stopped": true,
  "owned_children_stopped": true
}

===== ranged-download.json =====
{
  "utc": "2026-09-22T22:25:52.306088+00:00",
  "source": "https://registry.ollama.ai/v2/library/deepseek-r1/blobs/sha256:e6a7edc1a4d7d9b2de136a221a57336b76316cfe53a252aeba814496c5ae439d",
  "expected_and_verified_sha256": "e6a7edc1a4d7d9b2de136a221a57336b76316cfe53a252aeba814496c5ae439d",
  "bytes": 5225373760,
  "part_bytes": 33554432,
  "parts": 156,
  "workers": 8,
  "elapsed_seconds_this_process": 894.5,
  "method": "HTTP Range from the public official Ollama registry, exact Content-Range for each resumable segment, full assembled SHA-256 verification. No model inference."
}

===== powershell-walkthrough.json =====
{
  "utc": "2026-09-22T22:37:18.755042+00:00",
  "runtime_version": {
    "version": "0.34.2"
  },
  "purpose": "Fourth generation request; unscored reader-facing command check, excluded from three first-task results and timings.",
  "command": "$request = @{\n    model = 'deepseek-r1:8b-0528-qwen3-q4_K_M'\n    prompt = 'Reply with exactly: Local DeepSeek ready.'\n    think = $true\n    stream = $false\n    keep_alive = '5m'\n    options = @{\n        num_ctx = 4096\n        num_predict = 2048\n        num_thread = 8\n        temperature = 0.6\n        top_p = 0.95\n        seed = 42\n    }\n} | ConvertTo-Json -Depth 5\n$answer = Invoke-RestMethod -Uri 'http://127.0.0.1:11435/api/generate' -Method Post -ContentType 'application/json' -Body $request -TimeoutSec 600\n$answer | ConvertTo-Json -Depth 8",
  "exit_code": 0,
  "stdout": "{\n    \"model\":  \"deepseek-r1:8b-0528-qwen3-q4_K_M\",\n    \"created_at\":  \"2026-09-22T22:37:18.6661729Z\",\n    \"response\":  \"Local DeepSeek ready.\",\n    \"thinking\":  \"Okay, the user just sent a simple message: \\\"Reply with exactly: Local DeepSeek ready.\\\"  \\n\\nHmm, this seems like a direct command or instruction. The phrasing is very specific-\\\"exactly\\\"-so they probably want a precise, literal response without any additions or interpretations.  \\n\\nThe message itself is short and to the point, almost like a system prompt. Maybe the user is testing how I handle strict instructions, or perhaps they\\u0027re setting up some kind of interaction protocol.  \\n\\nI\\u0027ll keep my reply minimal and match the tone. A straightforward acknowledgment should work best here, since that\\u0027s what they\\u0027re asking for.  \\n\\nAlright, responding with \\\"Local DeepSeek ready.\\\" seems appropriate-it\\u0027s concise and directly addresses their request.  \\n\\nI wonder if there\\u0027s more to this query. If the user intended this as a greeting or a signal to start something, they might follow up with additional details. But for now, sticking exactly to their words is the way to go.\\n\",\n    \"done\":  true,\n    \"done_reason\":  \"stop\",\n    \"context\":  [\n                    151643,\n                    151669,\n                    20841,\n                    448,\n                    6896,\n                    25,\n                    8774,\n                    18183,\n                    39350,\n                    5527,\n                    13,\n                    151670,\n                    151667,\n                    198,\n                    32313,\n                    11,\n                    279,\n                    1196,\n                    1101,\n                    3208,\n                    264,\n                    4285,\n                    1943,\n                    25,\n                    330,\n                    20841,\n                    448,\n                    6896,\n                    25,\n                    8774,\n                    18183,\n                    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\"total_duration\":  69015332900,\n    \"load_duration\":  7929071700,\n    \"prompt_eval_count\":  12,\n    \"prompt_eval_cached_count\":  0,\n    \"prompt_eval_duration\":  435792000,\n    \"eval_count\":  207,\n    \"eval_duration\":  60628151000\n}\n",
  "stderr": "",
  "client_seconds": 69.69297400000505,
  "parsed_response": {
    "model": "deepseek-r1:8b-0528-qwen3-q4_K_M",
    "created_at": "2026-09-22T22:37:18.6661729Z",
    "response": "Local DeepSeek ready.",
    "thinking": "Okay, the user just sent a simple message: \"Reply with exactly: Local DeepSeek ready.\"  \n\nHmm, this seems like a direct command or instruction. The phrasing is very specific-\"exactly\"-so they probably want a precise, literal response without any additions or interpretations.  \n\nThe message itself is short and to the point, almost like a system prompt. Maybe the user is testing how I handle strict instructions, or perhaps they're setting up some kind of interaction protocol.  \n\nI'll keep my reply minimal and match the tone. A straightforward acknowledgment should work best here, since that's what they're asking for.  \n\nAlright, responding with \"Local DeepSeek ready.\" seems appropriate-it's concise and directly addresses their request.  \n\nI wonder if there's more to this query. If the user intended this as a greeting or a signal to start something, they might follow up with additional details. But for now, sticking exactly to their words is the way to go.\n",
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    "done_reason": "stop",
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    "total_duration": 69015332900,
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  "cli_ps_before_stop": {
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    "stdout": "NAME                                ID              SIZE      PROCESSOR    CONTEXT    UNTIL              \ndeepseek-r1:8b-0528-qwen3-q4_K_M    6995872bfe4c    5.9 GB    100% CPU     4096       4 minutes from now    \n",
    "stderr": ""
  },
  "cli_stop": {
    "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"
  },
  "cli_ps_after_stop": {
    "exit_code": 0,
    "stdout": "NAME    ID    SIZE    PROCESSOR    CONTEXT    UNTIL \n",
    "stderr": ""
  },
  "loaded_models_after_stop": {
    "models": []
  }
}

===== walkthrough-cleanup.json =====
{
  "server_stopped": true
}

===== unloaded-models.json =====
{
  "models": []
}

===== partial-cleanup.json =====
{
  "reason": "The complete official blob had already passed full SHA-256 verification.",
  "scope": "Only partial files for the exact blob in this isolated project-owned model cache.",
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