NousResearch__Nous-Hermes-2-SOLAR-10.7B/results_2024-07-01T22-46-11.267534.json

{
  "results": {
    "hellaswag": {
      "acc,none": 0.6467835092611034,
      "acc_stderr,none": 0.004769924131304646,
      "acc_norm,none": 0.8324039036048596,
      "acc_norm_stderr,none": 0.0037274387865133193,
      "alias": "hellaswag"
    },
    "eq_bench": {
      "eqbench,none": 63.524669890379585,
      "eqbench_stderr,none": 2.5941253832883335,
      "percent_parseable,none": 100.0,
      "percent_parseable_stderr,none": 0.0,
      "alias": "eq_bench"
    }
  },
  "group_subtasks": {
    "eq_bench": [],
    "hellaswag": []
  },
  "configs": {
    "eq_bench": {
      "task": "eq_bench",
      "dataset_path": "pbevan11/EQ-Bench",
      "validation_split": "validation",
      "doc_to_text": "prompt",
      "doc_to_target": "reference_answer_fullscale",
      "process_results": "def calculate_score_fullscale(docs, results):\n    reference = eval(docs[\"reference_answer_fullscale\"])\n    user = dict(re.findall(r\"(\\w+):\\s+(\\d+)\", results[0]))\n    # First check that the emotions specified in the answer match those in the reference\n    if len(user.items()) != 4:\n        # print('! Error: 4 emotions were not returned')\n        # print(user)\n        return {\"eqbench\": 0, \"percent_parseable\": 0}\n    emotions_dict = {}\n    for emotion, user_emotion_score in user.items():\n        for i in range(1, 5):\n            if emotion == reference[f\"emotion{i}\"]:\n                emotions_dict[emotion] = True\n    if len(emotions_dict) != 4:\n        print(\"! Error: emotions did not match reference\")\n        print(user)\n        return {\"eqbench\": 0, \"percent_parseable\": 0}\n\n    difference_tally = (\n        0  # Tally of differerence from reference answers for this question\n    )\n\n    # Iterate over each emotion in the user's answers.\n    for emotion, user_emotion_score in user.items():\n        # If this emotion is in the reference, calculate the difference between the user's score and the reference score.\n        for i in range(1, 5):\n            if emotion == reference[f\"emotion{i}\"]:\n                d = abs(\n                    float(user_emotion_score) - float(reference[f\"emotion{i}_score\"])\n                )\n                # this will be a value between 0 and 10\n                if d == 0:\n                    scaled_difference = 0\n                elif d <= 5:\n                    # S-shaped scaling function\n                    # https://www.desmos.com/calculator\n                    # 6.5\\cdot\\ \\frac{1}{\\left(1\\ +\\ e^{\\left(-1.2\\cdot\\left(x-4\\right)\\right)}\\right)}\n                    scaled_difference = 6.5 * (1 / (1 + math.e ** (-1.2 * (d - 4))))\n\n                else:\n                    scaled_difference = d\n                difference_tally += scaled_difference\n\n    # Inverting the difference tally so that the closer the answer is to reference, the higher the score.\n    # The adjustment constant is chosen such that answering randomly produces a score of zero.\n    adjust_const = 0.7477\n    final_score = 10 - (difference_tally * adjust_const)\n    final_score_percent = final_score * 10\n\n    return {\"eqbench\": final_score_percent, \"percent_parseable\": 100}\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "eqbench",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "percent_parseable",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "do_sample": false,
        "temperature": 0.0,
        "max_gen_toks": 80,
        "until": [
          "\n\n"
        ]
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 2.1
      }
    },
    "hellaswag": {
      "task": "hellaswag",
      "group": [
        "multiple_choice"
      ],
      "dataset_path": "hellaswag",
      "training_split": "train",
      "validation_split": "validation",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc):\n        ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n        out_doc = {\n            \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n            \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n            \"gold\": int(doc[\"label\"]),\n        }\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "{{query}}",
      "doc_to_target": "{{label}}",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "eq_bench": 2.1,
    "hellaswag": 1.0
  },
  "n-shot": {
    "eq_bench": 0,
    "hellaswag": 0
  },
  "higher_is_better": {
    "eq_bench": {
      "eqbench": true,
      "percent_parseable": true
    },
    "hellaswag": {
      "acc": true,
      "acc_norm": true
    }
  },
  "n-samples": {
    "hellaswag": {
      "original": 10042,
      "effective": 10042
    },
    "eq_bench": {
      "original": 171,
      "effective": 171
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=NousResearch/Nous-Hermes-2-SOLAR-10.7B,trust_remote_code=True",
    "model_num_parameters": 10731540480,
    "model_dtype": "torch.bfloat16",
    "model_revision": "main",
    "model_sha": "14c1fbe2f71acdcd58247b30d5439bd572d52386",
    "batch_size": "auto",
    "batch_sizes": [
      16
    ],
    "device": "cuda:0",
    "use_cache": null,
    "limit": null,
    "bootstrap_iters": 100000,
    "gen_kwargs": null,
    "random_seed": 0,
    "numpy_seed": 1234,
    "torch_seed": 1234,
    "fewshot_seed": 1234
  },
  "git_hash": null,
  "date": 1719871760.8771381,
  "pretty_env_info": "PyTorch version: 2.3.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.5.0-1022-gcp-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.1.105\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA L4\nGPU 1: NVIDIA L4\n\nNvidia driver version: 555.42.02\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture:                       x86_64\nCPU op-mode(s):                     32-bit, 64-bit\nAddress sizes:                      46 bits physical, 48 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             48\nOn-line CPU(s) list:                0-47\nVendor ID:                          GenuineIntel\nModel name:                         Intel(R) Xeon(R) CPU @ 2.20GHz\nCPU family:                         6\nModel:                              85\nThread(s) per core:                 2\nCore(s) per socket:                 24\nSocket(s):                          1\nStepping:                           7\nBogoMIPS:                           4400.47\nFlags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities\nHypervisor vendor:                  KVM\nVirtualization type:                full\nL1d cache:                          768 KiB (24 instances)\nL1i cache:                          768 KiB (24 instances)\nL2 cache:                           24 MiB (24 instances)\nL3 cache:                           38.5 MiB (1 instance)\nNUMA node(s):                       1\nNUMA node0 CPU(s):                  0-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit:        Not affected\nVulnerability L1tf:                 Not affected\nVulnerability Mds:                  Mitigation; Clear CPU buffers; SMT Host state unknown\nVulnerability Meltdown:             Not affected\nVulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Retbleed:             Mitigation; Enhanced IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT Host state unknown\n\nVersions of relevant libraries:\n[pip3] numpy==2.0.0\n[pip3] torch==2.3.1\n[pip3] triton==2.3.1\n[conda] Could not collect",
  "transformers_version": "4.41.2",
  "upper_git_hash": null,
  "tokenizer_pad_token": [
    "</s>",
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  ],
  "tokenizer_eos_token": [
    "<|im_end|>",
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  ],
  "tokenizer_bos_token": [
    "<s>",
    1
  ],
  "eot_token_id": 32000,
  "max_length": 4096,
  "task_hashes": {},
  "model_source": "hf",
  "model_name": "NousResearch/Nous-Hermes-2-SOLAR-10.7B",
  "model_name_sanitized": "NousResearch__Nous-Hermes-2-SOLAR-10.7B",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 83055.962424888,
  "end_time": 85273.196902306,
  "total_evaluation_time_seconds": "2217.2344774179946"
}