本站实际执行报告 · 不是目标产品原生界面截图ollama-modelfile-local-support
Ollama 0.40.9
- 旧Gemma list有条目但show/API show报GGUF缺失，未当作可用模型。
- 原生create qr-tutorial-support-proof成功，客服营业时间实际答09:00–18:00。
- 未提供退货窗口，实际答Unknown。
明确限制
- SYSTEM是提示约束，不是训练或可靠的权限边界；只实跑两个原创问题。
- 隔离Ollama0.40.9；模型为官方Qwen3:0.6b完整GGUF，经原生ollama create导入为tutorial-qwen3-proof，非完整OCI pull成功。权重SHA256 7f4030143c1c477224c5434f8272c662a8b042079a0a584f0a27a1684fe2e1fa，522640096字节；原生自动识别模板，capabilities tools/thinking/completion。
原始输出
create-execution.json
{
"exitCode": 0,
"stdout": "",
"stderr": "\u001b[?2026h\u001b[?25l\u001b[1Ggathering model components \u001b[K\nusing existing layer sha256:7f4030143c1c477224c5434f8272c662a8b042079a0a584f0a27a1684fe2e1fa \u001b[K\ncreating new layer sha256:367e0b50775915b57bf4ec40b11c0b131f16cd9e9a0dd4064d0f8e131386bb46 \u001b[K\ncreating new layer sha256:20e044ec1ba3f368c73e3d28cdb003cf88f61a583b246d41db0866c8b1a1cf66 \u001b[K\nwriting manifest \u001b[K\nsuccess \u001b[K\u001b[?25h\u001b[?2026l\n",
"command": [
"ollama",
"create",
"qr-tutorial-support-proof",
"-f",
"Modelfile"
]
}
result.json
{
"model": "qr-tutorial-support-proof",
"created_at": "2026-10-23T15:01:29.306652Z",
"message": {
"role": "assistant",
"content": "The fictional store opens at 09:00 AM and closes at 18:00 PM. If there are no specific hours provided, the default is 09:00 to 18:00."
},
"done": true,
"done_reason": "stop",
"total_duration": 8474859417,
"load_duration": 885959,
"prompt_eval_count": 76,
"prompt_eval_cached_count": 1,
"prompt_eval_duration": 40901000,
"eval_count": 49,
"eval_duration": 401959000
}
unknown-policy-result.json
{
"input": {
"model": "qr-tutorial-support-proof",
"messages": [
{
"role": "user",
"content": "What is this fictional store return window in days? Answer only using supplied facts. /no_think"
}
],
"stream": false,
"think": false,
"options": {
"temperature": 0,
"num_predict": 80
}
},
"result": {
"model": "qr-tutorial-support-proof",
"created_at": "2026-10-23T15:04:09.975895Z",
"message": {
"role": "assistant",
"content": "Unknown."
},
"done": true,
"done_reason": "stop",
"total_duration": 52941568875,
"load_duration": 59959666,
"prompt_eval_count": 81,
"prompt_eval_cached_count": 53,
"prompt_eval_duration": 24447000,
"eval_count": 3,
"eval_duration": 40482000
}
}
model-show.json
{
"modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this, replace FROM with:\n# FROM qr-tutorial-support-proof:latest\n\nFROM /Volumes/Mac Storage/Projects/tokenba-tutorial30-proof-20261023/ollama-models/blobs/sha256-7f4030143c1c477224c5434f8272c662a8b042079a0a584f0a27a1684fe2e1fa\nTEMPLATE {{ .Prompt }}\nSYSTEM You are a fictional stationery store support assistant. Only answer using supplied facts. Store hours are 09:00 to 18:00. If facts are missing say unknown. /no_think\nPARAMETER num_ctx 4096\nPARAMETER num_predict 256\nPARAMETER temperature 0\n",
"parameters": "num_ctx 4096\nnum_predict 256\ntemperature 0",
"template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
"system": "You are a fictional stationery store support assistant. Only answer using supplied facts. Store hours are 09:00 to 18:00. If facts are missing say unknown. /no_think",
"template_source": "gguf_chat_template",
"details": {
"parent_model": "tutorial-qwen3-proof:latest",
"format": "gguf",
"family": "qwen3",
"families": [
"qwen3"
],
"parameter_size": "751.63M",
"quantization_level": "Q4_K_M",
"runner": "llamacpp"
},
"model_info": {
"general.architecture": "qwen3",
"general.basename": "Qwen3",
"general.file_type": 15,
"general.license": "apache-2.0",
"general.parameter_count": 751632384,
"general.quantization_version": 2,
"general.size_label": "0.6B",
"general.type": "model",
"qwen3.attention.head_count": 16,
"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": 28,
"qwen3.context_length": 40960,
"qwen3.embedding_length": 1024,
"qwen3.feed_forward_length": 3072,
"qwen3.rope.freq_base": 1000000,
"tokenizer.ggml.add_bos_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": 151643,
"tokenizer.ggml.pre": "qwen2",
"tokenizer.ggml.token_type": [],
"tokenizer.ggml.tokens": []
},
"capabilities": [
"tools",
"thinking",
"completion"
],
"modified_at": "2026-10-23T23:01:20.806411243+08:00"
}