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| "model": "Nex-N2.5-mini-imatrix-Q4_0_ROCMFP4_FAST.gguf", |
| "draft": null, |
| "nmax": null, |
| "strict": false, |
| "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", |
| "dev": "Vulkan0", |
| "ctx": 65536, |
| "workload": "code", |
| "load_s": 20.0, |
| "cmd": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin/llama-server -m /mnt/models/nex-n2.5-mini/out-imat/Nex-N2.5-mini-imatrix-Q4_0_ROCMFP4_FAST.gguf -dev Vulkan0 -ngl 999 -fa on -dio --jinja -fit off --parallel 1 -c 65536 -b 2048 -ub 1024 --host 127.0.0.1 --port 18600 --no-webui", |
| "tg_median": 68.44, |
| "tg_min": 68.42, |
| "tg_max": 68.56, |
| "pp_median": 993.9, |
| "prompt_n": 7098, |
| "accept": null, |
| "prompt_n_min": 7098, |
| "prompt_n_max": 7102 |
| }, |
| { |
| "label": "n-q106-rocm-prose", |
| "model": "Nex-N2.5-mini-Q4_0_ROCMFP4_STRIX_LEAN.gguf", |
| "draft": null, |
| "nmax": null, |
| "strict": false, |
| "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", |
| "dev": "ROCm0", |
| "ctx": 65536, |
| "workload": "prose", |
| "load_s": 22.0, |
| "cmd": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin/llama-server -m /mnt/models/nex-n2.5-mini/out/Nex-N2.5-mini-Q4_0_ROCMFP4_STRIX_LEAN.gguf -dev ROCm0 -ngl 999 -fa on -dio --jinja -fit off --parallel 1 -c 65536 -b 2048 -ub 1024 --host 127.0.0.1 --port 18600 --no-webui", |
| "tg_median": 63.06, |
| "tg_min": 62.94, |
| "tg_max": 63.09, |
| "pp_median": 1139.1, |
| "prompt_n": 7454, |
| "accept": null, |
| "prompt_n_min": 7453, |
| "prompt_n_max": 7455 |
| }, |
| { |
| "label": "n-q106-vk-prose", |
| "model": "Nex-N2.5-mini-Q4_0_ROCMFP4_STRIX_LEAN.gguf", |
| "draft": null, |
| "nmax": null, |
| "strict": false, |
| "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", |
| "dev": "Vulkan0", |
| "ctx": 65536, |
| "workload": "prose", |
| "load_s": 22.0, |
| "cmd": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin/llama-server -m /mnt/models/nex-n2.5-mini/out/Nex-N2.5-mini-Q4_0_ROCMFP4_STRIX_LEAN.gguf -dev Vulkan0 -ngl 999 -fa on -dio --jinja -fit off --parallel 1 -c 65536 -b 2048 -ub 1024 --host 127.0.0.1 --port 18600 --no-webui", |
| "tg_median": 67.34, |
| "tg_min": 67.31, |
| "tg_max": 67.35, |
| "pp_median": 977.7, |
| "prompt_n": 7454, |
| "accept": null, |
| "prompt_n_min": 7454, |
| "prompt_n_max": 7457 |
| } |
| ], |
| "gates": [ |
| { |
| "label": "n-c3-q106", |
| "gate": "cachegate3-aligned", |
| "n": 5, |
| "L": 7039, |
| "expect_cache_n": 6011, |
| "all_reused": true, |
| "all_aligned": true, |
| "identical": 5, |
| "rows": [ |
| { |
| "variant": 0, |
| "L": 7039, |
| "warm_prompt_n": 1028, |
| "warm_cache_n": 6011, |
| "cold_prompt_n": 7039, |
| "cold_cache_n": 0, |
| "aligned": true, |
| "warm_draft": [ |
| null, |
| null |
| ], |
| "cold_draft": [ |
| null, |
| null |
| ], |
| "identical": true, |
| "first_diff_char": null, |
| "warm_sha": "013b7b4662ba", |
| "cold_sha": "013b7b4662ba", |
| "instr": "Write a function that lists every tensor name in this file. ok ok ok ok", |
| "warm_instr": "Summarise this file. (warm-up 0) ok ok ok ok", |
| "warm_text": "```python\ndef list_tensor_names(model: ModelBase) -> list[str]:\n \"\"\"Return every tensor name indexed by the model, in model-tensor order.\"\"\"\n return list(model.model_tensors.keys())\n```\n\nExample usage:\n\n```python\nmodel = SomeModelClass(\n dir_model=Path(\"...\"),\n ftype=gguf.LlamaFileType.GUESSED,\n fname_out=Path(\"model.gguf\"),\n)\n\nfor name in list_tensor_names(model):\n print(name)\n```\n\nIf you want to include tensors that are only produced later by `modify_tensors()` or quantization dequantization, use this version instead:\n\n```python\ndef list_all_tensor_names(model: ModelBase) -> list[str]:\n \"\"\"Return tensor names from indexing plus tensors produced by tensor transforms.\"\"\"\n names = set(model.model_tensors.keys())\n\n for name, gen in model.model", |
| "cold_text": "```python\ndef list_tensor_names(model: ModelBase) -> list[str]:\n \"\"\"Return every tensor name indexed by the model, in model-tensor order.\"\"\"\n return list(model.model_tensors.keys())\n```\n\nExample usage:\n\n```python\nmodel = SomeModelClass(\n dir_model=Path(\"...\"),\n ftype=gguf.LlamaFileType.GUESSED,\n fname_out=Path(\"model.gguf\"),\n)\n\nfor name in list_tensor_names(model):\n print(name)\n```\n\nIf you want to include tensors that are only produced later by `modify_tensors()` or quantization dequantization, use this version instead:\n\n```python\ndef list_all_tensor_names(model: ModelBase) -> list[str]:\n \"\"\"Return tensor names from indexing plus tensors produced by tensor transforms.\"\"\"\n names = set(model.model_tensors.keys())\n\n for name, gen in model.model", |
| "warm_prompt_ms": 999.89, |
| "cold_prompt_ms": 6158.66 |
| }, |
| { |
| "variant": 1, |
| "L": 7039, |
| "warm_prompt_n": 1028, |
| "warm_cache_n": 6011, |
| "cold_prompt_n": 7039, |
| "cold_cache_n": 0, |
| "aligned": true, |
| "warm_draft": [ |
| null, |
| null |
| ], |
| "cold_draft": [ |
| null, |
| null |
| ], |
| "identical": true, |
| "first_diff_char": null, |
| "warm_sha": "76498f09c9c8", |
| "cold_sha": "76498f09c9c8", |
| "instr": "Write a function that counts the model classes registered in this file. ok ok ok", |
| "warm_instr": "Summarise this file. (warm-up 1) ok ok ok ok", |
| "warm_text": "```python\ndef count_model_classes(path: str | os.PathLike[str]) -> int:\n \"\"\"Count classes that inherit from ModelBase in the given Python file.\"\"\"\n with open(path, \"r\", encoding=\"utf-8\") as f:\n tree = ast.parse(f.read(), filename=path)\n\n count = 0\n\n for node in tree.body:\n if not isinstance(node, ast.ClassDef):\n continue\n\n for base in node.bases:\n if isinstance(base, ast.Name) and base.id == \"ModelBase\":\n count += 1\n break\n\n return count\n```\n\nExample usage:\n\n```python\nif __name__ == \"__main__\":\n print(count_model_classes(\"your_file.py\"))\n```\n\nThis counts only direct subclasses of `ModelBase`, not indirect subclasses. If you want to count indirect subclasses too, use this version:", |
| "cold_text": "```python\ndef count_model_classes(path: str | os.PathLike[str]) -> int:\n \"\"\"Count classes that inherit from ModelBase in the given Python file.\"\"\"\n with open(path, \"r\", encoding=\"utf-8\") as f:\n tree = ast.parse(f.read(), filename=path)\n\n count = 0\n\n for node in tree.body:\n if not isinstance(node, ast.ClassDef):\n continue\n\n for base in node.bases:\n if isinstance(base, ast.Name) and base.id == \"ModelBase\":\n count += 1\n break\n\n return count\n```\n\nExample usage:\n\n```python\nif __name__ == \"__main__\":\n print(count_model_classes(\"your_file.py\"))\n```\n\nThis counts only direct subclasses of `ModelBase`, not indirect subclasses. If you want to count indirect subclasses too, use this version:", |
| "warm_prompt_ms": 1002.97, |
| "cold_prompt_ms": 6165.72 |
| }, |
| { |
| "variant": 2, |
| "L": 7039, |
| "warm_prompt_n": 1028, |
| "warm_cache_n": 6011, |
| "cold_prompt_n": 7039, |
| "cold_cache_n": 0, |
| "aligned": true, |
| "warm_draft": [ |
| null, |
| null |
| ], |
| "cold_draft": [ |
| null, |
| null |
| ], |
| "identical": true, |
| "first_diff_char": null, |
| "warm_sha": "16577469a2ea", |
| "cold_sha": "16577469a2ea", |
| "instr": "Write a function that finds the longest method in this file. ok ok ok ok", |
| "warm_instr": "Summarise this file. (warm-up 2) ok ok ok ok", |
| "warm_text": "```python\nimport ast\nfrom pathlib import Path\n\n\ndef find_longest_method(path: str | Path) -> tuple[str, int, ast.FunctionDef | ast.AsyncFunctionDef]:\n \"\"\"\n Return the longest method in a Python file.\n\n Length is measured by the number of AST nodes in the method body.\n \"\"\"\n source_path = Path(path)\n tree = ast.parse(source_path.read_text(encoding=\"utf-8\"), filename=str(source_path))\n\n methods = [\n node\n for node in ast.walk(tree)\n if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))\n ]\n\n if not methods:\n raise ValueError(f\"No methods found in {source_path}\")\n\n longest = max(\n methods,\n key=lambda node: sum(1 for _ in ast.walk(node.body)),\n )\n\n return (\n longest.name", |
| "cold_text": "```python\nimport ast\nfrom pathlib import Path\n\n\ndef find_longest_method(path: str | Path) -> tuple[str, int, ast.FunctionDef | ast.AsyncFunctionDef]:\n \"\"\"\n Return the longest method in a Python file.\n\n Length is measured by the number of AST nodes in the method body.\n \"\"\"\n source_path = Path(path)\n tree = ast.parse(source_path.read_text(encoding=\"utf-8\"), filename=str(source_path))\n\n methods = [\n node\n for node in ast.walk(tree)\n if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))\n ]\n\n if not methods:\n raise ValueError(f\"No methods found in {source_path}\")\n\n longest = max(\n methods,\n key=lambda node: sum(1 for _ in ast.walk(node.body)),\n )\n\n return (\n longest.name", |
| "warm_prompt_ms": 1003.48, |
| "cold_prompt_ms": 6169.04 |
| }, |
| { |
| "variant": 3, |
| "L": 7039, |
| "warm_prompt_n": 1028, |
| "warm_cache_n": 6011, |
| "cold_prompt_n": 7039, |
| "cold_cache_n": 0, |
| "aligned": true, |
| "warm_draft": [ |
| null, |
| null |
| ], |
| "cold_draft": [ |
| null, |
| null |
| ], |
| "identical": true, |
| "first_diff_char": null, |
| "warm_sha": "a8545d39788d", |
| "cold_sha": "a8545d39788d", |
| "instr": "Write a function that returns every regular expression used in this file. ok ok ok", |
| "warm_instr": "Summarise this file. (warm-up 3) ok ok ok ok", |
| "warm_text": "```python\nimport re\n\ndef extract_regular_expressions(source: str) -> list[str]:\n \"\"\"\n Return every regular expression string literal used in the given Python source.\n\n This is a lexical scanner, not a full AST parser, so it handles normal string\n literals but does not evaluate them.\n \"\"\"\n expressions: list[str] = []\n i = 0\n n = len(source)\n\n while i < n:\n if source[i] not in \"'\\\"\":\n i += 1\n continue\n\n quote = source[i]\n i += 1\n chars: list[str] = []\n\n while i < n:\n ch = source[i]\n\n if ch == \"\\\\\":\n if i + 1 >= n:\n break\n chars.append(ch)\n chars.append(source[i + 1])\n i", |
| "cold_text": "```python\nimport re\n\ndef extract_regular_expressions(source: str) -> list[str]:\n \"\"\"\n Return every regular expression string literal used in the given Python source.\n\n This is a lexical scanner, not a full AST parser, so it handles normal string\n literals but does not evaluate them.\n \"\"\"\n expressions: list[str] = []\n i = 0\n n = len(source)\n\n while i < n:\n if source[i] not in \"'\\\"\":\n i += 1\n continue\n\n quote = source[i]\n i += 1\n chars: list[str] = []\n\n while i < n:\n ch = source[i]\n\n if ch == \"\\\\\":\n if i + 1 >= n:\n break\n chars.append(ch)\n chars.append(source[i + 1])\n i", |
| "warm_prompt_ms": 1003.44, |
| "cold_prompt_ms": 6170.8 |
| }, |
| { |
| "variant": 4, |
| "L": 7039, |
| "warm_prompt_n": 1028, |
| "warm_cache_n": 6011, |
| "cold_prompt_n": 7039, |
| "cold_cache_n": 0, |
| "aligned": true, |
| "warm_draft": [ |
| null, |
| null |
| ], |
| "cold_draft": [ |
| null, |
| null |
| ], |
| "identical": true, |
| "first_diff_char": null, |
| "warm_sha": "af97b2dde4e2", |
| "cold_sha": "af97b2dde4e2", |
| "instr": "Write a function that maps each class in this file to its base classes. ok", |
| "warm_instr": "Summarise this file. (warm-up 4) ok ok ok ok", |
| "warm_text": "```python\ndef map_classes_to_base_classes(source: str) -> dict[str, list[str]]:\n \"\"\"\n Return a mapping of class names to their direct base class names.\n\n Example:\n class A(B): pass\n class C(A): pass\n returns:\n {\"A\": [\"B\"], \"C\": [\"A\"]}\n \"\"\"\n import ast\n\n tree = ast.parse(source)\n result: dict[str, list[str]] = {}\n\n for node in tree.body:\n if isinstance(node, ast.ClassDef):\n bases = [ast.unparse(base) for base in node.bases]\n result[node.name] = bases\n\n return result\n```\n\nUsage:\n\n```python\nsource = open(\"file.py\", \"r\", encoding=\"utf-8\").read()\nprint(map_classes_to_base_classes(source))\n```\n\nFor the provided file", |
| "cold_text": "```python\ndef map_classes_to_base_classes(source: str) -> dict[str, list[str]]:\n \"\"\"\n Return a mapping of class names to their direct base class names.\n\n Example:\n class A(B): pass\n class C(A): pass\n returns:\n {\"A\": [\"B\"], \"C\": [\"A\"]}\n \"\"\"\n import ast\n\n tree = ast.parse(source)\n result: dict[str, list[str]] = {}\n\n for node in tree.body:\n if isinstance(node, ast.ClassDef):\n bases = [ast.unparse(base) for base in node.bases]\n result[node.name] = bases\n\n return result\n```\n\nUsage:\n\n```python\nsource = open(\"file.py\", \"r\", encoding=\"utf-8\").read()\nprint(map_classes_to_base_classes(source))\n```\n\nFor the provided file", |
| "warm_prompt_ms": 1005.61, |
| "cold_prompt_ms": 6177.63 |
| } |
| ], |
| "result": "PASS" |
| }, |
| { |
| "label": "n-tools-q106", |
| "passed": 6, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": false, |
| "nested-object|think=True": false, |
| "enum|think=True": false, |
| "correct-decline|think=True": false, |
| "multi-turn|think=True": false, |
| "streaming|think=True": false, |
| "parallel|think=True": false, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": false, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-vision-q106-faon", |
| "fa": "on", |
| "mtp": false, |
| "expected": "red,blue,circle,square", |
| "answer": "The image shows two shapes: a red circle on the left and a blue square on the right.", |
| "hits": [ |
| "red", |
| "blue", |
| "circle", |
| "square" |
| ], |
| "error": null, |
| "server_died": false, |
| "server_log_errors": [], |
| "result": "PASS" |
| }, |
| { |
| "label": "n-vision-q106-faoff", |
| "fa": "off", |
| "mtp": false, |
| "expected": "red,blue,circle,square", |
| "answer": "The image shows two simple shapes:\n\n- A **red circle** on the left.\n- A **blue square** on the right.", |
| "hits": [ |
| "red", |
| "blue", |
| "circle", |
| "square" |
| ], |
| "error": null, |
| "server_died": false, |
| "server_log_errors": [], |
| "result": "PASS" |
| }, |
| { |
| "label": "n-tools-q106-tpl", |
| "passed": 14, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": true, |
| "nested-object|think=True": true, |
| "enum|think=True": true, |
| "correct-decline|think=True": true, |
| "multi-turn|think=True": true, |
| "streaming|think=True": true, |
| "parallel|think=True": true, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-tools-q106-tpl-medium", |
| "passed": 7, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": false, |
| "nested-object|think=True": false, |
| "enum|think=True": false, |
| "correct-decline|think=True": false, |
| "multi-turn|think=True": false, |
| "streaming|think=True": false, |
| "parallel|think=True": false, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-tools-q106-c1", |
| "passed": 13, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": true, |
| "nested-object|think=True": false, |
| "enum|think=True": true, |
| "correct-decline|think=True": true, |
| "multi-turn|think=True": true, |
| "streaming|think=True": true, |
| "parallel|think=True": true, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-vision-q106-c1-faon", |
| "fa": "on", |
| "mtp": false, |
| "expected": "red,blue,circle,square", |
| "answer": "The image shows two shapes: a red circle on the left and a blue square on the right.", |
| "hits": [ |
| "red", |
| "blue", |
| "circle", |
| "square" |
| ], |
| "error": null, |
| "server_died": false, |
| "server_log_errors": [], |
| "result": "PASS" |
| }, |
| { |
| "label": "n-tools-q106-roff", |
| "passed": 13, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": true, |
| "nested-object|think=True": false, |
| "enum|think=True": true, |
| "correct-decline|think=True": true, |
| "multi-turn|think=True": true, |
| "streaming|think=True": true, |
| "parallel|think=True": true, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-tools-q106-roff-r2", |
| "passed": 13, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": true, |
| "nested-object|think=True": true, |
| "enum|think=True": true, |
| "correct-decline|think=True": true, |
| "multi-turn|think=True": true, |
| "streaming|think=True": true, |
| "parallel|think=True": false, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-tools-q106-roff-r3", |
| "passed": 14, |
| "total": 14, |
| "detail": { |
| "multi-arg|think=True": true, |
| "nested-object|think=True": true, |
| "enum|think=True": true, |
| "correct-decline|think=True": true, |
| "multi-turn|think=True": true, |
| "streaming|think=True": true, |
| "parallel|think=True": true, |
| "multi-arg|think=False": true, |
| "nested-object|think=False": true, |
| "enum|think=False": true, |
| "correct-decline|think=False": true, |
| "multi-turn|think=False": true, |
| "streaming|think=False": true, |
| "parallel|think=False": true |
| } |
| }, |
| { |
| "label": "n-vision-q106-roff-faon", |
| "fa": "on", |
| "mtp": false, |
| "expected": "red,blue,circle,square", |
| "answer": "The image shows two shapes: a red circle on the left and a blue square on the right.", |
| "hits": [ |
| "red", |
| "blue", |
| "circle", |
| "square" |
| ], |
| "error": null, |
| "server_died": false, |
| "server_log_errors": [], |
| "result": "PASS" |
| } |
| ], |
| "sizing": [ |
| { |
| "label": "strix-lean", |
| "ctx": 65536, |
| "avail_before": 122.34, |
| "footprint_loaded_gib": 21.11, |
| "footprint_after_8k_gib": 21.29 |
| }, |
| { |
| "label": "strix-lean", |
| "ctx": 262144, |
| "avail_before": 122.15, |
| "footprint_loaded_gib": 24.36, |
| "footprint_after_8k_gib": 24.52 |
| } |
| ], |
| "n_ubatch": 1024, |
| "template_fix": { |
| "file": "chat_template_enable_thinking.jinja", |
| "size_bytes": 7895, |
| "sha256": "9183c7ba8510fb9628edd2265a4cad8c02b3aecec3cbdf65620f47494e75836d", |
| "source_sha256": "f1753536417ee87cded4bc5017354eb2123c079d20c634403a3d808b2ba3cc5b", |
| "shim": "{%- if reasoning_effort is not defined and enable_thinking is defined %}{%- set reasoning_effort = 'high' if enable_thinking else 'none' %}{%- endif %}\n", |
| "server_flags": [ |
| "--chat-template-file", |
| "chat_template_enable_thinking.jinja", |
| "--reasoning", |
| "off" |
| ], |
| "probes_roff": { |
| "no-kwargs|correct-decline": { |
| "content": "391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "no-kwargs|single-word": { |
| "content": "ready", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "no-kwargs|multi-arg": { |
| "content": "", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [] |
| }, |
| "enable_thinking=false|correct-decline": { |
| "content": "391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "enable_thinking=false|single-word": { |
| "content": "ready", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "enable_thinking=false|multi-arg": { |
| "content": "", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [] |
| }, |
| "reasoning_effort=high|correct-decline": { |
| "content": "We need answer directly. 391.\n</think>\n\n391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=high|single-word": { |
| "content": "We need need output exactly ready.\n</think>\n\nready", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=high|multi-arg": { |
| "content": "We need need tool. Current weather Paris celsius.\n</think>\n\n", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=medium|correct-decline": { |
| "content": "\n\n</think>\n\n391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=medium|single-word": { |
| "content": "\n\n</think>\n\nready", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=medium|multi-arg": { |
| "content": "\n\n</think>\n\n", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [ |
| "</think>" |
| ] |
| }, |
| "reasoning_effort=none|correct-decline": { |
| "content": "391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "reasoning_effort=none|single-word": { |
| "content": "ready", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "reasoning_effort=none|multi-arg": { |
| "content": "", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [] |
| } |
| }, |
| "probes_high_default_on": { |
| "no-kwargs|correct-decline": { |
| "content": "391", |
| "reasoning_len": 30, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "no-kwargs|multi-arg": { |
| "content": "", |
| "reasoning_len": 50, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [] |
| }, |
| "reasoning_effort=medium|correct-decline": { |
| "content": "391", |
| "reasoning_len": 0, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "reasoning_effort=medium|multi-arg": { |
| "content": "", |
| "reasoning_len": 0, |
| "tool_calls": [ |
| "get_weather" |
| ], |
| "leaks": [] |
| }, |
| "reasoning_effort=none|correct-decline": { |
| "content": "", |
| "reasoning_len": 3, |
| "tool_calls": [], |
| "leaks": [] |
| }, |
| "reasoning_effort=none|multi-arg": { |
| "content": "", |
| "reasoning_len": 133, |
| "tool_calls": [], |
| "leaks": [] |
| } |
| }, |
| "medium_mapping_label": "n-tools-q106-tpl-medium" |
| }, |
| "tools_diag": { |
| "stock_on_replies": 7, |
| "stock_on_leaks": 7, |
| "stock_on_reasoning_extracted": 0, |
| "nested_off_attempts": 4, |
| "nested_off_http500": 2, |
| "gate_http500_logged": true, |
| "flag_probes": { |
| "default": { |
| "leaks": 3, |
| "n": 3 |
| }, |
| "fmt-deepseek": { |
| "leaks": 3, |
| "n": 3 |
| }, |
| "srv-kwargs-high": { |
| "leaks": 3, |
| "n": 3 |
| }, |
| "reasoning-on": { |
| "leaks": 3, |
| "n": 3 |
| }, |
| "tpl-enable-thinking": { |
| "leaks": 0, |
| "n": 3 |
| } |
| } |
| }, |
| "seats": { |
| "max1-nex-fast": { |
| "unit": "max1-nex-fast", |
| "port": 8097, |
| "load_s": 25, |
| "time": "2026-09-17T01:21:10Z", |
| "direct_reply": "ready", |
| "direct_tg": 41.41386950489719, |
| "default_reply": "ready", |
| "default_reasoning_len": 0, |
| "default_leak": false, |
| "thinking_reply": "", |
| "thinking_reasoning_len": 5, |
| "thinking_leak": false, |
| "gateway_model": "nex-n2.5-mini-fast@max1", |
| "gateway_reply": "ready", |
| "result": "PASS" |
| }, |
| "max1-nex-fast-imat": { |
| "unit": "max1-nex-fast-imat", |
| "port": 8098, |
| "load_s": 25, |
| "time": "2026-09-17T01:21:43Z", |
| "direct_reply": "ready", |
| "direct_tg": 41.54290343352097, |
| "default_reply": "ready", |
| "default_reasoning_len": 0, |
| "default_leak": false, |
| "thinking_reply": "", |
| "thinking_reasoning_len": 5, |
| "thinking_leak": false, |
| "gateway_model": "nex-n2.5-mini-fast-imatrix@max1", |
| "gateway_reply": "ready", |
| "result": "PASS" |
| } |
| }, |
| "measured": "2026-09-16", |
| "measured_range": [ |
| "2026-09-16", |
| "2026-09-17" |
| ] |
| } |
