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"header_keys_only_in_standard": [] }, "q103i": { "file": "Nex-N2.5-mini-imatrix-Q4_0-ROCmFP4-FAST.gguf", "local_file": "Nex-N2.5-mini-imatrix-Q4_0_ROCMFP4_FAST.gguf", "dir": "out-imat", "ftype": 103, "size_bytes": 18648496064, "quant_mib": 17774.11, "bpw": 4.3, "quant_seconds": 328.50694, "imatrix_entries": 510, "readback": "PASS", "arch": "qwen35moe", "tensors": 733, "nextn_tensors": 0, "output_weight": "Q6_K", "token_embd": "Q4_0_ROCMFP4_FAST", "quality_measured": true, "ppl": 6.368075, "ppl_err": 0.077317, "ppl_ratio": 1.02233, "kld_mean": 0.088974, "kld_err": 0.001318, "kld_p99": 0.784672, "kld_median": 0.039738, "same_top_p": 87.454, "rms_dp": 8.37, "vk": { "ppl": 6.392929, "ppl_err": 0.077851, "ppl_ratio": 1.026321, "kld_mean": 0.08915, "kld_err": 0.001266, "kld_p99": 0.784911, "kld_median": 0.039583, "same_top_p": 87.424, "rms_dp": 8.509 }, "same_tensor_types_as_standard": true, "same_tensor_names_types_bytes_as_standard": true, "file_size_delta_bytes": 256, "differs_from_standard": true, "header_keys_only_in_imatrix": [ "quantize.imatrix.chunks_count", "quantize.imatrix.dataset", "quantize.imatrix.entries_count", "quantize.imatrix.file" ], "header_keys_only_in_standard": [] } }, "aux": { "mmproj-Nex-N2.5-mini-BF16.gguf": 902821920, "chat_template_enable_thinking.jinja": 7895 }, "mmproj": { "file": "mmproj-Nex-N2.5-mini-BF16.gguf", "size_bytes": 902821920, "elements": 446571248, "readback": "PASS", "arch": "clip", "ftype": 32, "tensors": 334 }, "bench": [ { "label": "n-q106-rocm", "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": "code", "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": 63.94, "tg_min": 63.56, "tg_max": 63.96, "pp_median": 1158.2, "prompt_n": 7098, "accept": null, "prompt_n_min": 7098, "prompt_n_max": 7100 }, { "label": "n-q106-vk", "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": "code", "load_s": 4.0, "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": 68.16, "tg_min": 68.1, "tg_max": 68.21, "pp_median": 1006.0, "prompt_n": 7096, "accept": null, "prompt_n_min": 7096, "prompt_n_max": 7098 }, { "label": "n-q102-rocm", "model": "Nex-N2.5-mini-Q4_0_ROCMFP4_COHERENT.gguf", "draft": null, "nmax": null, "strict": false, "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", "dev": "ROCm0", "ctx": 65536, "workload": "code", "load_s": 24.0, "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_COHERENT.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": 61.74, "tg_min": 61.74, "tg_max": 61.8, "pp_median": 1186.7, "prompt_n": 7100, "accept": null, "prompt_n_min": 7096, "prompt_n_max": 7100 }, { "label": "n-q102-vk", "model": "Nex-N2.5-mini-Q4_0_ROCMFP4_COHERENT.gguf", "draft": null, "nmax": null, "strict": false, "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", "dev": "Vulkan0", "ctx": 65536, "workload": "code", "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_COHERENT.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": 68.0, "tg_min": 67.88, "tg_max": 68.09, "pp_median": 1001.4, "prompt_n": 7098, "accept": null, "prompt_n_min": 7098, "prompt_n_max": 7101 }, { "label": "n-q103-rocm", "model": "Nex-N2.5-mini-Q4_0_ROCMFP4_FAST.gguf", "draft": null, "nmax": null, "strict": false, "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", "dev": "ROCm0", "ctx": 65536, "workload": "code", "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_FAST.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": 63.31, "tg_min": 63.14, "tg_max": 63.38, "pp_median": 1156.6, "prompt_n": 7100, "accept": null, "prompt_n_min": 7094, "prompt_n_max": 7100 }, { "label": "n-q103-vk", "model": "Nex-N2.5-mini-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/Nex-N2.5-mini-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": 68.62, "tg_min": 68.6, "tg_max": 68.67, "pp_median": 998.9, "prompt_n": 7098, "accept": null, "prompt_n_min": 7098, "prompt_n_max": 7101 }, { "label": "n-q106i-rocm", "model": "Nex-N2.5-mini-imatrix-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": "code", "load_s": 22.0, "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_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": 63.29, "tg_min": 63.19, "tg_max": 63.41, "pp_median": 1145.3, "prompt_n": 7100, "accept": null, "prompt_n_min": 7097, "prompt_n_max": 7100 }, { "label": "n-q106i-vk", "model": "Nex-N2.5-mini-imatrix-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": "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_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": 67.77, "tg_min": 67.74, "tg_max": 67.86, "pp_median": 995.0, "prompt_n": 7098, "accept": null, "prompt_n_min": 7098, "prompt_n_max": 7100 }, { "label": "n-q102i-rocm", "model": "Nex-N2.5-mini-imatrix-Q4_0_ROCMFP4_COHERENT.gguf", "draft": null, "nmax": null, "strict": false, "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", "dev": "ROCm0", "ctx": 65536, "workload": "code", "load_s": 24.0, "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_COHERENT.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": 61.56, "tg_min": 61.53, "tg_max": 61.63, "pp_median": 1183.6, "prompt_n": 7098, "accept": null, "prompt_n_min": 7096, "prompt_n_max": 7100 }, { "label": "n-q102i-vk", "model": "Nex-N2.5-mini-imatrix-Q4_0_ROCMFP4_COHERENT.gguf", "draft": null, "nmax": null, "strict": false, "bin": "/opt/llama-rocm/rocmfpx-724/build-hipvk/bin", "dev": "Vulkan0", "ctx": 65536, "workload": "code", "load_s": 22.0, "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_COHERENT.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": 67.81, "tg_min": 67.77, "tg_max": 67.85, "pp_median": 998.7, "prompt_n": 7097, "accept": null, "prompt_n_min": 7096, "prompt_n_max": 7097 }, { "label": "n-q103i-rocm", "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": "ROCm0", "ctx": 65536, "workload": "code", "load_s": 22.0, "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 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": 63.03, "tg_min": 62.55, "tg_max": 63.11, "pp_median": 1154.4, "prompt_n": 7101, "accept": null, "prompt_n_min": 7099, "prompt_n_max": 7101 }, { "label": "n-q103i-vk", "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\n\n391", "reasoning_len": 0, "tool_calls": [], "leaks": [ "" ] }, "reasoning_effort=high|single-word": { "content": "We need need output exactly ready.\n\n\nready", "reasoning_len": 0, "tool_calls": [], "leaks": [ "" ] }, "reasoning_effort=high|multi-arg": { "content": "We need need tool. Current weather Paris celsius.\n\n\n", "reasoning_len": 0, "tool_calls": [ "get_weather" ], "leaks": [ "" ] }, "reasoning_effort=medium|correct-decline": { "content": "\n\n\n\n391", "reasoning_len": 0, "tool_calls": [], "leaks": [ "" ] }, "reasoning_effort=medium|single-word": { "content": "\n\n\n\nready", "reasoning_len": 0, "tool_calls": [], "leaks": [ "" ] }, "reasoning_effort=medium|multi-arg": { "content": "\n\n\n\n", "reasoning_len": 0, "tool_calls": [ "get_weather" ], "leaks": [ "" ] }, "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" ] }