Formatting, Hermes-2-Pro-Llama-3-8B-GGUF

IFEval.csv

@@ -1,6 +1,7 @@
-Model,Parameters (B),Repo,Quantization,Final Score,Strict Prompt Score,Strict Inst Score,Loose Prompt Score,Loose Inst Score,Link
+Model,Params (B),Repo,Quantization,Final Score,Strict Prompt Score,Strict Inst Score,Loose Prompt Score,Loose Inst Score,Link
 Llama 3 8B,8,failspy/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF,Q8_0,0.7589,0.7001,0.7818,0.7394,0.8141,https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3-GGUF
 Llama 3 8B,8,MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF,Q8_0,0.7366,0.6765,0.7614,0.7172,0.7914,https://huggingface.co/MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF
 Phi 3 Medium 4K,14,bartowski/Phi-3-medium-4k-instruct-GGUF,Q8_0,0.6673,0.6100,0.7014,0.6322,0.7254,https://huggingface.co/bartowski/Phi-3-medium-4k-instruct-GGUF
 Mistral 7B v0.3,7,MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF,Q8_0,0.5689,0.4972,0.5983,0.5397,0.6403,https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF
+Llama 3 8B,8,bartowski/Hermes-2-Pro-Llama-3-8B-GGUF,Q8_0,0.5686,0.5010,0.6079,0.5287,0.6367,https://huggingface.co/bartowski/Hermes-2-Pro-Llama-3-8B-GGUF
 Mistral 7B v0.1,7,TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF,Q8_0,0.5473,0.4750,0.5995,0.4935,0.6211,https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF

app.py

@@ -19,7 +19,7 @@ def load_leaderboard_table_csv(filename, add_hyperlink=True):
             for h, v in zip(heads, row):
                 if "Score" in h:
                     item[h] = float(v)
-                elif h != "Model" and h != "Parameters (B)" and h != "Repo" and h != "Quantization" and h != "Link":
+                elif h != "Model" and h != "Params (B)" and h != "Repo" and h != "Quantization" and h != "Link":
                     item[h] = int(v)
                 else:
                     item[h] = v
@@ -42,7 +42,7 @@ def get_arena_table(model_table_df):
         row.append(model_name)
 
         row.append(
-            model_table_df["Parameters (B)"].values[model_key]
+            model_table_df["Params (B)"].values[model_key]
         )
         row.append(
             model_table_df["Repo"].values[model_key]
@@ -86,7 +86,7 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
                     headers=[
                         "Rank",
                         "Model",
-                        "Parameters (B)",
+                        "Params (B)",
                         "Repo",
                         "Quantization",
                         "Final Score",
@@ -110,7 +110,7 @@ def build_leaderboard_tab(leaderboard_table_file, show_plot=False):
                     value=arena_table_vals,
                     elem_id="arena_leaderboard_dataframe",
                     height=700,
-                    column_widths=[50, 150, 100, 150, 100, 100, 100, 100, 100, 100],
+                    column_widths=[50, 160, 60, 230, 100, 90, 90, 90, 90, 90],
                     wrap=True,
                 )
 

results/Hermes-2-Pro-Llama-3-8B-GGUF.json

@@ -0,0 +1,117 @@
+{
+  "results": {
+    "ifeval": {
+      "prompt_level_strict_acc,none": 0.5009242144177449,
+      "prompt_level_strict_acc_stderr,none": 0.02151653738784254,
+      "inst_level_strict_acc,none": 0.6079136690647482,
+      "inst_level_strict_acc_stderr,none": "N/A",
+      "prompt_level_loose_acc,none": 0.5286506469500925,
+      "prompt_level_loose_acc_stderr,none": 0.021481220930080487,
+      "inst_level_loose_acc,none": 0.6366906474820144,
+      "inst_level_loose_acc_stderr,none": "N/A",
+      "alias": "ifeval"
+    }
+  },
+  "group_subtasks": {
+    "ifeval": []
+  },
+  "configs": {
+    "ifeval": {
+      "task": "ifeval",
+      "dataset_path": "wis-k/instruction-following-eval",
+      "test_split": "train",
+      "doc_to_text": "prompt",
+      "doc_to_target": 0,
+      "process_results": "def process_results(doc, results):\n    eval_logger.warning(\n        \"This task is meant for chat-finetuned models, and may not give meaningful results for models other than `openai` or `anthropic` if `doc_to_text` in its YAML is not wrapped in the appropriate chat template string. This warning will be removed when chat templating support is added natively to local models\"\n    )\n\n    inp = InputExample(\n        key=doc[\"key\"],\n        instruction_id_list=doc[\"instruction_id_list\"],\n        prompt=doc[\"prompt\"],\n        kwargs=doc[\"kwargs\"],\n    )\n    response = results[0]\n\n    out_strict = test_instruction_following_strict(inp, response)\n    out_loose = test_instruction_following_loose(inp, response)\n\n    return {\n        \"prompt_level_strict_acc\": out_strict.follow_all_instructions,\n        \"inst_level_strict_acc\": out_strict.follow_instruction_list,\n        \"prompt_level_loose_acc\": out_loose.follow_all_instructions,\n        \"inst_level_loose_acc\": out_loose.follow_instruction_list,\n    }\n",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "prompt_level_strict_acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "inst_level_strict_acc",
+          "aggregation": "def agg_inst_level_acc(items):\n    flat_items = [item for sublist in items for item in sublist]\n    inst_level_acc = sum(flat_items) / len(flat_items)\n    return inst_level_acc\n",
+          "higher_is_better": true
+        },
+        {
+          "metric": "prompt_level_loose_acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "inst_level_loose_acc",
+          "aggregation": "def agg_inst_level_acc(items):\n    flat_items = [item for sublist in items for item in sublist]\n    inst_level_acc = sum(flat_items) / len(flat_items)\n    return inst_level_acc\n",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "generate_until",
+      "generation_kwargs": {
+        "until": [],
+        "do_sample": false,
+        "temperature": 0.0,
+        "max_gen_toks": 1280
+      },
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 2.0
+      }
+    }
+  },
+  "versions": {
+    "ifeval": 2.0
+  },
+  "n-shot": {
+    "ifeval": 0
+  },
+  "higher_is_better": {
+    "ifeval": {
+      "prompt_level_strict_acc": true,
+      "inst_level_strict_acc": true,
+      "prompt_level_loose_acc": true,
+      "inst_level_loose_acc": true
+    }
+  },
+  "n-samples": {
+    "ifeval": {
+      "original": 541,
+      "effective": 541
+    }
+  },
+  "config": {
+    "model": "local-chat-completions",
+    "model_args": "base_url=http://localhost:7860/v1",
+    "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": "e39df01c",
+  "date": 1717664594.213845,
+  "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: Arch Linux (x86_64)\nGCC version: (GCC) 14.1.1 20240522\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.39\n\nPython version: 3.12.3 (main, Apr 23 2024, 09:16:07) [GCC 13.2.1 20240417] (64-bit runtime)\nPython platform: Linux-6.9.2-arch1-1-x86_64-with-glibc2.39\nIs CUDA available: False\nCUDA runtime version: No CUDA\nCUDA_MODULE_LOADING set to: N/A\nGPU models and configuration: No CUDA\nNvidia driver version: No CUDA\ncuDNN version: No CUDA\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:                        43 bits physical, 48 bits virtual\nByte Order:                           Little Endian\nCPU(s):                               16\nOn-line CPU(s) list:                  0-15\nVendor ID:                            AuthenticAMD\nModel name:                           AMD Ryzen 7 3700X 8-Core Processor\nCPU family:                           23\nModel:                                113\nThread(s) per core:                   2\nCore(s) per socket:                   8\nSocket(s):                            1\nStepping:                             0\nFrequency boost:                      enabled\nCPU(s) scaling MHz:                   80%\nCPU max MHz:                          4426.1709\nCPU min MHz:                          2200.0000\nBogoMIPS:                             7189.76\nFlags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es\nVirtualization:                       AMD-V\nL1d cache:                            256 KiB (8 instances)\nL1i cache:                            256 KiB (8 instances)\nL2 cache:                             4 MiB (8 instances)\nL3 cache:                             32 MiB (2 instances)\nNUMA node(s):                         1\nNUMA node0 CPU(s):                    0-15\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 Reg file data sampling: Not affected\nVulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection\nVulnerability Spec rstack overflow:   Mitigation; Safe RET\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; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds:                  Not affected\nVulnerability Tsx async abort:        Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.3.1\n[conda] Could not collect",
+  "transformers_version": "4.41.2",
+  "upper_git_hash": null,
+  "task_hashes": {},
+  "model_source": "local-chat-completions",
+  "model_name": "",
+  "model_name_sanitized": "",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "chat_template": null,
+  "chat_template_sha": null,
+  "start_time": 607849.767653735,
+  "end_time": 610198.327449187,
+  "total_evaluation_time_seconds": "2348.559795452049"
+}