Delete .__Models__cognitivecomputations__samantha-mistral-instruct-7b

.__Models__cognitivecomputations__samantha-mistral-instruct-7b/results_2024-07-02T19-23-24.931098.json

@@ -1,177 +0,0 @@
-{
-  "results": {
-    "hellaswag": {
-      "acc,none": 0.5549691296554471,
-      "acc_stderr,none": 0.004959535443170614,
-      "acc_norm,none": 0.7365066719776937,
-      "acc_norm_stderr,none": 0.004396273173717414,
-      "alias": "hellaswag"
-    },
-    "eq_bench": {
-      "eqbench,none": 18.697258501455963,
-      "eqbench_stderr,none": 3.7651200808947474,
-      "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=./Models/cognitivecomputations/samantha-mistral-instruct-7b",
-    "model_num_parameters": 7241732096,
-    "model_dtype": "torch.bfloat16",
-    "model_revision": "main",
-    "model_sha": "",
-    "batch_size": "auto",
-    "batch_sizes": [
-      64
-    ],
-    "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": 1719946631.0748322,
-  "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==1.26.4\n[pip3] torch==2.3.1\n[pip3] triton==2.3.1\n[conda] Could not collect",
-  "transformers_version": "4.42.3",
-  "upper_git_hash": null,
-  "tokenizer_pad_token": [
-    "<unk>",
-    0
-  ],
-  "tokenizer_eos_token": [
-    "</s>",
-    2
-  ],
-  "tokenizer_bos_token": [
-    "<s>",
-    1
-  ],
-  "eot_token_id": 2,
-  "max_length": 32768,
-  "task_hashes": {},
-  "model_source": "hf",
-  "model_name": "./Models/cognitivecomputations/samantha-mistral-instruct-7b",
-  "model_name_sanitized": ".__Models__cognitivecomputations__samantha-mistral-instruct-7b",
-  "system_instruction": null,
-  "system_instruction_sha": null,
-  "fewshot_as_multiturn": false,
-  "chat_template": null,
-  "chat_template_sha": null,
-  "start_time": 157926.171562761,
-  "end_time": 159506.860535868,
-  "total_evaluation_time_seconds": "1580.6889731069969"
-}