Repository: EleutherAI/lm-evaluation-harness
Issue: Bad Performance of llama-2-70b in  Humaneval Benchmark
Root cause: Configuration Error

I use  command below to test llama-2-70b-hf model with humaneval benchmark

```bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
export HF_ALLOW_CODE_EVAL="1"
python -m lm_eval    \
 --model hf     \
--model_args pretrained=/home/shared/models/meta-llama/Llama-2-70b-hf/,parallelize=True   \
  --tasks humaneval  \
   --confirm_run_unsafe_code   \
  --show_config   \
  --log_samples   \
  --batch_size 1 \
 --output_path ./eval
```

but the result is very unusual with  only 0.0183 pass@1, here are my result and configuration
```bash
INFO:lm_eval.loggers.evaluation_tracker:Saving results aggregated
INFO:lm_eval.loggers.evaluation_tracker:Saving per-sample results for: humaneval
hf (pretrained=/home/shared/models/meta-llama/Llama-2-70b-hf/,parallelize=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|  Filter   |n-shot|Metric|   |Value |   |Stderr|
|---------|------:|-----------|-----:|------|---|-----:|---|-----:|
|humaneval|      1|create_test|     0|pass@1|   |0.0183|±  |0.0105|
```
```json
{
  "results": {
    "humaneval": {
      "alias": "humaneval",
      "pass@1,create_test": 0.018292682926829267,
      "pass@1_stderr,create_test": 0.010496292269168305
    }
  },
  "group_subtasks": {
    "humaneval": []
  },
  "configs": {
    "humaneval": {
      "task": "humaneval",
      "dataset_path": "openai/openai_humaneval",
      "test_split": "test",
      "doc_to_text": "{{prompt}}",
      "doc_to_target": "{{test}}\ncheck({{entry_point}})",
      "unsafe_code": true,
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "def pass_at_k(references: list[str], predictions: list[list[str]], k: list[int] = None):\n    global compute_\n    assert k is not None\n    if isinstance(k, int):\n        k = [k]\n    res = compute_.compute(\n        references=references,\n        predictions=predictions,\n        k=k,\n    )\n    return res[0]\n",
          "aggregation": "mean",
          "higher_is_better": true,
          "k": [
            1
          ]
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "\nclass",
          "\ndef",
          "\n#",
          "\nif",
          "\nprint"
        ],
        "max_gen_toks": 1024,
        "do_sample": false
      },
      "repeats": 1,
      "filter_list": [
        {
          "name": "create_test",
          "filter": [
            {
              "function": "custom",
              "filter_fn": "<function build_predictions at 0x7f81e4c91120>"
            }
          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0,
        "pretrained": "/home/shared/models/meta-llama/Llama-2-70b-hf/",
        "parallelize": true
      }
    }
  },
  "versions": {
    "humaneval": 1.0
  },
  "n-shot": {
    "humaneval": 0
  },
  "higher_is_better": {
    "humaneval": {
      "pass_at_k": true
    }
  },
  "n-samples": {
    "humaneval": {
      "original": 164,
      "effective": 164
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=/home/shared/models/meta-llama/Llama-2-70b-hf/,parallelize=True",
    "model_num_parameters": 68976648192,
    "model_dtype": "torch.float16",
    "model_revision": "main",
    "model_sha": "",
    "batch_size": "1",
    "batch_sizes": [],
    "device": null,
    "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": "7ddb2b1",
  "date": 1764037905.2037978,
    "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.2 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version: Could not collect\nCMake version: version 4.1.0\nLibc version: glibc-2.39\n\nPython version: 3.12.3 (main, Feb  4 2025, 14:48:35) [GCC 13.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H20-3e\nGPU 1: NVIDIA H20-3e\nGPU 2: NVIDIA H20-3e\nGPU 3: NVIDIA H20-3e\nGPU 4: NVIDIA H20-3e\nGPU 5: NVIDIA H20-3e\nGPU 6: NVIDIA H20-3e\nGPU 7: NVIDIA H20-3e\n\nNvidia driver version: 550.144.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.11.0\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:                      52 bits physical, 57 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             240\nOn-line CPU(s) list:                0-239\nVendor ID:                          GenuineIntel\nModel name:                         Intel(R) Xeon(R) 6759P-C\nCPU family:                         6\nModel:                              173\nThread(s) per core:                 2\nCore(s) per socket:                 60\nSocket(s):                          2\nStepping:                           1\nBogoMIPS:                           5200.00\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 monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities\nHypervisor vendor:                  KVM\nVirtualization type:                full\nL1d cache:                          5.6 MiB (120 instances)\nL1i cache:                          7.5 MiB (120 instances)\nL2 cache:                           240 MiB (120 instances)\nL3 cache:                           640 MiB (2 instances)\nNUMA node(s):                       2\nNUMA node0 CPU(s):                  0-119\nNUMA node1 CPU(s):                  120-239\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit:        Not affected\nVulnerability L1tf:                 Not affected\nVulnerability Mds:                  Not affected\nVulnerability Meltdown:             Not affected\nVulnerability Mmio stale data:      Not affected\nVulnerability Retbleed:             Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Mitigation; TSX disabled\n\nVersions of relevant libraries:\n[pip3] Could not collect\n[conda] Could not collect",
  "transformers_version": "4.57.1",
  "lm_eval_version": "0.4.9.1",
  "upper_git_hash": null,
  "tokenizer_pad_token": [
    "<unk>",
    "0"
  ],
  "tokenizer_eos_token": [
    "
",
    "2"
  ],
  "tokenizer_bos_token": [
    "
",
    "1"
  ],
  "eot_token_id": 2,
  "max_length": 4096
}
```
I want to know are there any additional configurations need to set before testing humaneval bench mark for llama2-70b-hf ? Please!