Upload folder using huggingface_hub

merge_universal.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+
+# Get current directory
+CURRENT_DIR=$(pwd)
+
+# Create merged_models directory if it doesn't exist
+mkdir -p "${CURRENT_DIR}/merged_models"
+
+# Find all checkpoint directories
+for checkpoint_dir in checkpoint-*; do
+    # Skip if not a directory
+    if [ ! -d "$checkpoint_dir" ]; then
+        continue
+    fi
+    
+    # Extract checkpoint name
+    checkpoint_name=$(basename "$checkpoint_dir")
+    
+    # Create output directory
+    mkdir -p "${CURRENT_DIR}/merged_models/${checkpoint_name}"
+    
+    echo "Processing ${checkpoint_name}..."
+    
+    # Run the merge command
+    python3 -m axolotl.cli.merge_lora model.yml \
+        --lora_model_dir="./${checkpoint_name}" \
+        --output_dir="${CURRENT_DIR}/merged_models/${checkpoint_name}"
+    
+    # Check if merge was successful
+    if [ -d "${CURRENT_DIR}/merged_models/${checkpoint_name}/merged" ]; then
+        # Move all files from merged directory up one level
+        mv "${CURRENT_DIR}/merged_models/${checkpoint_name}/merged"/* \
+           "${CURRENT_DIR}/merged_models/${checkpoint_name}/"
+        
+        # Remove empty merged directory
+        rmdir "${CURRENT_DIR}/merged_models/${checkpoint_name}/merged"
+        
+        echo "Successfully processed ${checkpoint_name}"
+    else
+        echo "Error processing ${checkpoint_name}"
+    fi
+done
+
+echo "All checkpoints processed!"

merged_models/checkpoint-2611/added_tokens.json

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+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

merged_models/checkpoint-2611/config.json

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+{
+  "_name_or_path": "ugaoo/peft_x8_7B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

merged_models/checkpoint-2611/generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.46.3"
+}

merged_models/checkpoint-2611/model-00001-of-00004.safetensors

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+      "special": true
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+      "special": true
+    },
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+      "lstrip": false,
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+    },
+    "151650": {
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+    },
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+      "special": true
+    },
+    "151652": {
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+      "lstrip": false,
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+      "rstrip": false,
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+    },
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+      "lstrip": false,
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+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+    },
+    "151660": {
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+    },
+    "151661": {
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+      "lstrip": false,
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+      "special": false
+    },
+    "151662": {
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+      "lstrip": false,
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+      "rstrip": false,
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+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

merged_models/eval.py

@@ -0,0 +1,122 @@
+import os
+import json
+import pandas as pd
+from typing import Dict, List
+import argparse
+
+# Add this at the beginning of the script
+parser = argparse.ArgumentParser(description='Process model results')
+parser.add_argument('--modelname', type=str, help='Model name to use as prefix')
+args = parser.parse_args()
+
+def find_result_files(root_dir: str) -> List[tuple]:
+    """
+    Find all results JSON files and their corresponding checkpoint numbers.
+    Returns list of (checkpoint_number, file_path) tuples.
+    """
+    result_files = []
+    
+    for root, dirs, files in os.walk(root_dir):
+        if 'result' in root.lower():
+            for file in files:
+                if file.startswith('result') and file.endswith('.json'):
+                    # Extract checkpoint number from path
+                    checkpoint = None
+                    path_parts = root.split(os.sep)
+                    for part in path_parts:
+                        if part.startswith('checkpoint-'):
+                            checkpoint = part
+                            break
+                    
+                    if checkpoint:
+                        result_files.append((checkpoint, os.path.join(root, file)))
+    
+    return result_files
+
+def extract_metrics(json_path: str) -> Dict[str, float]:
+    """
+    Extract specific accuracy metrics from a results JSON file and convert to percentages.
+    """
+    with open(json_path, 'r') as f:
+        data = json.load(f)
+    
+    metrics = {}
+    target_tasks = [
+        'medmcqa',
+        'medqa_4options',
+        'mmlu_anatomy',
+        'mmlu_clinical_knowledge',
+        'mmlu_college_biology',
+        'mmlu_college_medicine',
+        'mmlu_medical_genetics',
+        'mmlu_professional_medicine',
+        'pubmedqa'
+    ]
+    
+    results = data.get('results', {})
+    for task in target_tasks:
+        if task in results:
+            # Convert to percentage and round to 3 decimal places
+            value = results[task].get('acc,none')
+            if value is not None:
+                metrics[task] = round(value * 100, 3)
+            else:
+                metrics[task] = None
+    
+    # Calculate average of available metrics
+    valid_metrics = [v for v in metrics.values() if v is not None]
+    if valid_metrics:
+        metrics['average'] = round(sum(valid_metrics) / len(valid_metrics), 3)
+    else:
+        metrics['average'] = None
+    
+    return metrics
+
+def process_all_results(root_dir: str, output_file: str = 'model_metrics.csv', checkpoint_prefix: str = None):
+    """
+    Process all result files and create a CSV with metrics as percentages.
+    
+    Parameters:
+        root_dir (str): Root directory to search for result files
+        output_file (str): Output CSV filename
+        checkpoint_prefix (str): Optional prefix to add before checkpoint numbers (e.g., "model_name_")
+    """
+    result_files = find_result_files(root_dir)
+    all_metrics = []
+    
+    for checkpoint, file_path in result_files:
+        metrics = extract_metrics(file_path)
+        # Add prefix to checkpoint if provided
+        if checkpoint_prefix:
+            metrics['checkpoint'] = f"{checkpoint_prefix}{checkpoint}"
+        else:
+            metrics['checkpoint'] = checkpoint
+        all_metrics.append(metrics)
+    
+    if all_metrics:
+        df = pd.DataFrame(all_metrics)
+        # Reorder columns to put checkpoint and average first
+        cols = ['checkpoint', 'average'] + [col for col in df.columns if col not in ['checkpoint', 'average']]
+        df = df[cols]
+        
+        # Format float columns to 3 decimal places
+        float_cols = [col for col in df.columns if col != 'checkpoint']
+        for col in float_cols:
+            df[col] = df[col].apply(lambda x: f"{x:.3f}" if pd.notnull(x) else x)
+            
+        df.to_csv(output_file, index=False)
+        print(f"Results saved to {output_file}")
+    else:
+        print("No result files found.")
+
+# Usage examples
+if __name__ == "__main__":
+    root_directory = "."  # Replace with your root directory path
+    
+    # Example 1: Without prefix (original behavior)
+    # process_all_results(root_directory)
+    
+    process_all_results(
+        root_directory,
+        output_file='model_metrics.csv',
+        checkpoint_prefix=args.modelname)

merged_models/eval.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+# Find all checkpoint directories and sort them numerically
+for checkpoint in $(ls -d checkpoint-* | sort -t '-' -k2 -n); do
+    echo "Evaluating $checkpoint..."
+    
+    lm_eval --model hf \
+        --model_args pretrained=$checkpoint,parallelize=True,trust_remote_code=True \
+        --tasks multimedqa \
+        --device cuda:0 \
+        --batch_size auto \
+        --output_path "results" \
+        --log_samples
+        
+    echo "Finished evaluating $checkpoint"
+    echo "----------------------------------------"
+done

merged_models/model_metrics.csv

@@ -0,0 +1,3 @@
+checkpoint,average,medmcqa,medqa_4options,mmlu_anatomy,mmlu_clinical_knowledge,mmlu_college_biology,mmlu_college_medicine,mmlu_medical_genetics,mmlu_professional_medicine,pubmedqa
+checkpoint-2611,76.449,59.264,68.028,71.852,82.264,86.111,74.566,87.000,82.353,76.600
+checkpoint-5222,76.163,57.805,67.321,70.370,81.132,87.500,73.988,88.000,82.353,77.000

merged_models/results/checkpoint-2611/results_2025-03-17T19-53-01.924844.json

@@ -0,0 +1,578 @@
+{
+  "results": {
+    "multimedqa": {
+      "acc,none": 0.6539389638041164,
+      "acc_stderr,none": 0.005579639659176411,
+      "alias": "multimedqa"
+    },
+    "medmcqa": {
+      "alias": " - medmcqa",
+      "acc,none": 0.5926368634950993,
+      "acc_stderr,none": 0.007597893115829174,
+      "acc_norm,none": 0.5926368634950993,
+      "acc_norm_stderr,none": 0.007597893115829174
+    },
+    "medqa_4options": {
+      "alias": " - medqa_4options",
+      "acc,none": 0.6802827965435978,
+      "acc_stderr,none": 0.013076279392845757,
+      "acc_norm,none": 0.6802827965435978,
+      "acc_norm_stderr,none": 0.013076279392845757
+    },
+    "mmlu_anatomy": {
+      "alias": " - anatomy (mmlu)",
+      "acc,none": 0.7185185185185186,
+      "acc_stderr,none": 0.03885004245800254
+    },
+    "mmlu_clinical_knowledge": {
+      "alias": " - clinical_knowledge (mmlu)",
+      "acc,none": 0.8226415094339623,
+      "acc_stderr,none": 0.023508739218846927
+    },
+    "mmlu_college_biology": {
+      "alias": " - college_biology (mmlu)",
+      "acc,none": 0.8611111111111112,
+      "acc_stderr,none": 0.02891980295613489
+    },
+    "mmlu_college_medicine": {
+      "alias": " - college_medicine (mmlu)",
+      "acc,none": 0.7456647398843931,
+      "acc_stderr,none": 0.0332055644308557
+    },
+    "mmlu_medical_genetics": {
+      "alias": " - medical_genetics (mmlu)",
+      "acc,none": 0.87,
+      "acc_stderr,none": 0.033799766898963086
+    },
+    "mmlu_professional_medicine": {
+      "alias": " - professional_medicine (mmlu)",
+      "acc,none": 0.8235294117647058,
+      "acc_stderr,none": 0.023157468308559345
+    },
+    "pubmedqa": {
+      "alias": " - pubmedqa",
+      "acc,none": 0.766,
+      "acc_stderr,none": 0.018952741564893707
+    }
+  },
+  "groups": {
+    "multimedqa": {
+      "acc,none": 0.6539389638041164,
+      "acc_stderr,none": 0.005579639659176411,
+      "alias": "multimedqa"
+    }
+  },
+  "group_subtasks": {
+    "multimedqa": [
+      "pubmedqa",
+      "medmcqa",
+      "medqa_4options",
+      "mmlu_anatomy",
+      "mmlu_clinical_knowledge",
+      "mmlu_college_medicine",
+      "mmlu_medical_genetics",
+      "mmlu_professional_medicine",
+      "mmlu_college_biology"
+    ]
+  },
+  "configs": {
+    "medmcqa": {
+      "task": "medmcqa",
+      "dataset_path": "medmcqa",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "validation",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    \"\"\"\n    Question: <question>\n    Choices:\n    A. <choice1>\n    B. <choice2>\n    C. <choice3>\n    D. <choice4>\n    Answer:\n    \"\"\"\n    choices = [doc[\"opa\"], doc[\"opb\"], doc[\"opc\"], doc[\"opd\"]]\n    option_choices = {\n        \"A\": choices[0],\n        \"B\": choices[1],\n        \"C\": choices[2],\n        \"D\": choices[3],\n    }\n\n    prompt = \"Question: \" + doc[\"question\"] + \"\\nChoices:\\n\"\n    for choice, option in option_choices.items():\n        prompt += f\"{choice.upper()}. {option}\\n\"\n    prompt += \"Answer:\"\n    return prompt\n",
+      "doc_to_target": "cop",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "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": true,
+      "doc_to_decontamination_query": "{{question}}"
+    },
+    "medqa_4options": {
+      "task": "medqa_4options",
+      "dataset_path": "GBaker/MedQA-USMLE-4-options-hf",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    option_choices = {\n        \"A\": doc[\"ending0\"],\n        \"B\": doc[\"ending1\"],\n        \"C\": doc[\"ending2\"],\n        \"D\": doc[\"ending3\"],\n    }\n    answers = \"\".join((f\"{k}. {v}\\n\") for k, v in option_choices.items())\n    return f\"Question: {doc['sent1']}\\n{answers}Answer:\"\n",
+      "doc_to_target": "def doc_to_target(doc) -> int:\n    return doc[\"label\"]\n",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "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
+    },
+    "mmlu_anatomy": {
+      "task": "mmlu_anatomy",
+      "task_alias": "anatomy (mmlu)",
+      "tag": "mmlu_stem_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "anatomy",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_clinical_knowledge": {
+      "task": "mmlu_clinical_knowledge",
+      "task_alias": "clinical_knowledge (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "clinical_knowledge",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_college_biology": {
+      "task": "mmlu_college_biology",
+      "task_alias": "college_biology (mmlu)",
+      "tag": "mmlu_stem_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_biology",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college biology.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_college_medicine": {
+      "task": "mmlu_college_medicine",
+      "task_alias": "college_medicine (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_medicine",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_medical_genetics": {
+      "task": "mmlu_medical_genetics",
+      "task_alias": "medical_genetics (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "medical_genetics",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_professional_medicine": {
+      "task": "mmlu_professional_medicine",
+      "task_alias": "professional_medicine (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "professional_medicine",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "pubmedqa": {
+      "task": "pubmedqa",
+      "dataset_path": "bigbio/pubmed_qa",
+      "dataset_name": "pubmed_qa_labeled_fold0_source",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    ctxs = \"\\n\".join(doc[\"CONTEXTS\"])\n    return \"Abstract: {}\\nQuestion: {}\\nAnswer:\".format(\n        ctxs,\n        doc[\"QUESTION\"],\n    )\n",
+      "doc_to_target": "final_decision",
+      "doc_to_choice": [
+        "yes",
+        "no",
+        "maybe"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "medmcqa": "Yaml",
+    "medqa_4options": "Yaml",
+    "mmlu_anatomy": 1.0,
+    "mmlu_clinical_knowledge": 1.0,
+    "mmlu_college_biology": 1.0,
+    "mmlu_college_medicine": 1.0,
+    "mmlu_medical_genetics": 1.0,
+    "mmlu_professional_medicine": 1.0,
+    "pubmedqa": 1.0
+  },
+  "n-shot": {
+    "medmcqa": 0,
+    "medqa_4options": 0,
+    "mmlu_anatomy": 0,
+    "mmlu_clinical_knowledge": 0,
+    "mmlu_college_biology": 0,
+    "mmlu_college_medicine": 0,
+    "mmlu_medical_genetics": 0,
+    "mmlu_professional_medicine": 0,
+    "pubmedqa": 0
+  },
+  "higher_is_better": {
+    "medmcqa": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "medqa_4options": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "mmlu_anatomy": {
+      "acc": true
+    },
+    "mmlu_clinical_knowledge": {
+      "acc": true
+    },
+    "mmlu_college_biology": {
+      "acc": true
+    },
+    "mmlu_college_medicine": {
+      "acc": true
+    },
+    "mmlu_medical_genetics": {
+      "acc": true
+    },
+    "mmlu_professional_medicine": {
+      "acc": true
+    },
+    "multimedqa": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "pubmedqa": {
+      "acc": true
+    }
+  },
+  "n-samples": {
+    "pubmedqa": {
+      "original": 500,
+      "effective": 500
+    },
+    "medmcqa": {
+      "original": 4183,
+      "effective": 4183
+    },
+    "medqa_4options": {
+      "original": 1273,
+      "effective": 1273
+    },
+    "mmlu_anatomy": {
+      "original": 135,
+      "effective": 135
+    },
+    "mmlu_clinical_knowledge": {
+      "original": 265,
+      "effective": 265
+    },
+    "mmlu_college_medicine": {
+      "original": 173,
+      "effective": 173
+    },
+    "mmlu_medical_genetics": {
+      "original": 100,
+      "effective": 100
+    },
+    "mmlu_professional_medicine": {
+      "original": 272,
+      "effective": 272
+    },
+    "mmlu_college_biology": {
+      "original": 144,
+      "effective": 144
+    }
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=checkpoint-2611,parallelize=True,trust_remote_code=True",
+    "model_num_parameters": 7615616512,
+    "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": "6915b12",
+  "date": 1742241081.119386,
+  "pretty_env_info": "PyTorch version: 2.5.1+cu124\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.31.0\nLibc version: glibc-2.35\n\nPython version: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.8.0-40-generic-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: GPU 0: NVIDIA H200\nNvidia driver version: 550.127.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0\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:                        46 bits physical, 57 bits virtual\nByte Order:                           Little Endian\nCPU(s):                               192\nOn-line CPU(s) list:                  0-191\nVendor ID:                            GenuineIntel\nModel name:                           INTEL(R) XEON(R) PLATINUM 8568Y+\nCPU family:                           6\nModel:                                207\nThread(s) per core:                   2\nCore(s) per socket:                   48\nSocket(s):                            2\nStepping:                             2\nCPU max MHz:                          4000.0000\nCPU min MHz:                          800.0000\nBogoMIPS:                             4600.00\nFlags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization:                       VT-x\nL1d cache:                            4.5 MiB (96 instances)\nL1i cache:                            3 MiB (96 instances)\nL2 cache:                             192 MiB (96 instances)\nL3 cache:                             600 MiB (2 instances)\nNUMA node(s):                         2\nNUMA node0 CPU(s):                    0-47,96-143\nNUMA node1 CPU(s):                    48-95,144-191\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:               Not affected\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 BHI_DIS_S\nVulnerability Srbds:                  Not affected\nVulnerability Tsx async abort:        Not affected\n\nVersions of relevant libraries:\n[pip3] galore-torch==1.0\n[pip3] lion-pytorch==0.1.2\n[pip3] numpy==1.26.4\n[pip3] torch==2.5.1\n[pip3] torch-optimi==0.2.1\n[pip3] torchao==0.7.0\n[pip3] triton==3.1.0\n[conda] No relevant packages",
+  "transformers_version": "4.46.3",
+  "upper_git_hash": null,
+  "tokenizer_pad_token": [
+    "<|endoftext|>",
+    "151643"
+  ],
+  "tokenizer_eos_token": [
+    "<|im_end|>",
+    "151645"
+  ],
+  "tokenizer_bos_token": [
+    null,
+    "None"
+  ],
+  "eot_token_id": 151645,
+  "max_length": 32768,
+  "task_hashes": {
+    "pubmedqa": "870d7e5614da5dcad21abd725786223f3f6c5f4b6c35263df2d926eb7705a57a",
+    "medmcqa": "4ef81a02eca79c63cacb070d9b6375e2a463961f416266b281c01bccf94e70ea",
+    "medqa_4options": "2a2e29c3d5c0f042309940f072ce2a0c60def924a527be58650dd55665880fd6",
+    "mmlu_anatomy": "8a394ba6aa4d3366637e72da67c7d4c0286d47cb371a4f4a9814259be8bbe3ad",
+    "mmlu_clinical_knowledge": "839bf7b05724190f7277a957e8b2183a7b4dc74ab9ca72063d10872092a1ea7a",
+    "mmlu_college_medicine": "14529d73333850b8be0fc1d4c102c4500b76434c8c761611be6899af27608455",
+    "mmlu_medical_genetics": "9b736fa6d447dd8f017f7e2dc81e7487f3412a8551075ca312e48db9c4c5e108",
+    "mmlu_professional_medicine": "b1c4eea40bd1d93e49c50cadd35db8bbb96392c40d208ae1ffd6e72c306d757a",
+    "mmlu_college_biology": "d983837a4ac4327e74ff7f131eda1f0c23f6c9f2a1088e3a5162c6ede31605d5"
+  },
+  "model_source": "hf",
+  "model_name": "checkpoint-2611",
+  "model_name_sanitized": "checkpoint-2611",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "fewshot_as_multiturn": false,
+  "chat_template": null,
+  "chat_template_sha": null,
+  "start_time": 8223588.771301923,
+  "end_time": 8223696.053149864,
+  "total_evaluation_time_seconds": "107.28184794075787"
+}

merged_models/results/checkpoint-5222/results_2025-03-17T19-54-40.045633.json

@@ -0,0 +1,578 @@
+{
+  "results": {
+    "multimedqa": {
+      "acc,none": 0.643860894251242,
+      "acc_stderr,none": 0.00560404846208839,
+      "alias": "multimedqa"
+    },
+    "medmcqa": {
+      "alias": " - medmcqa",
+      "acc,none": 0.5780540282094191,
+      "acc_stderr,none": 0.007636961906055535,
+      "acc_norm,none": 0.5780540282094191,
+      "acc_norm_stderr,none": 0.007636961906055535
+    },
+    "medqa_4options": {
+      "alias": " - medqa_4options",
+      "acc,none": 0.6732128829536528,
+      "acc_stderr,none": 0.013151191960556162,
+      "acc_norm,none": 0.6732128829536528,
+      "acc_norm_stderr,none": 0.013151191960556162
+    },
+    "mmlu_anatomy": {
+      "alias": " - anatomy (mmlu)",
+      "acc,none": 0.7037037037037037,
+      "acc_stderr,none": 0.03944624162501116
+    },
+    "mmlu_clinical_knowledge": {
+      "alias": " - clinical_knowledge (mmlu)",
+      "acc,none": 0.8113207547169812,
+      "acc_stderr,none": 0.02407999513006223
+    },
+    "mmlu_college_biology": {
+      "alias": " - college_biology (mmlu)",
+      "acc,none": 0.875,
+      "acc_stderr,none": 0.02765610492929436
+    },
+    "mmlu_college_medicine": {
+      "alias": " - college_medicine (mmlu)",
+      "acc,none": 0.7398843930635838,
+      "acc_stderr,none": 0.033450369167889904
+    },
+    "mmlu_medical_genetics": {
+      "alias": " - medical_genetics (mmlu)",
+      "acc,none": 0.88,
+      "acc_stderr,none": 0.03265986323710906
+    },
+    "mmlu_professional_medicine": {
+      "alias": " - professional_medicine (mmlu)",
+      "acc,none": 0.8235294117647058,
+      "acc_stderr,none": 0.02315746830855935
+    },
+    "pubmedqa": {
+      "alias": " - pubmedqa",
+      "acc,none": 0.77,
+      "acc_stderr,none": 0.018839050391123206
+    }
+  },
+  "groups": {
+    "multimedqa": {
+      "acc,none": 0.643860894251242,
+      "acc_stderr,none": 0.00560404846208839,
+      "alias": "multimedqa"
+    }
+  },
+  "group_subtasks": {
+    "multimedqa": [
+      "pubmedqa",
+      "medmcqa",
+      "medqa_4options",
+      "mmlu_anatomy",
+      "mmlu_clinical_knowledge",
+      "mmlu_college_medicine",
+      "mmlu_medical_genetics",
+      "mmlu_professional_medicine",
+      "mmlu_college_biology"
+    ]
+  },
+  "configs": {
+    "medmcqa": {
+      "task": "medmcqa",
+      "dataset_path": "medmcqa",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "validation",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    \"\"\"\n    Question: <question>\n    Choices:\n    A. <choice1>\n    B. <choice2>\n    C. <choice3>\n    D. <choice4>\n    Answer:\n    \"\"\"\n    choices = [doc[\"opa\"], doc[\"opb\"], doc[\"opc\"], doc[\"opd\"]]\n    option_choices = {\n        \"A\": choices[0],\n        \"B\": choices[1],\n        \"C\": choices[2],\n        \"D\": choices[3],\n    }\n\n    prompt = \"Question: \" + doc[\"question\"] + \"\\nChoices:\\n\"\n    for choice, option in option_choices.items():\n        prompt += f\"{choice.upper()}. {option}\\n\"\n    prompt += \"Answer:\"\n    return prompt\n",
+      "doc_to_target": "cop",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "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": true,
+      "doc_to_decontamination_query": "{{question}}"
+    },
+    "medqa_4options": {
+      "task": "medqa_4options",
+      "dataset_path": "GBaker/MedQA-USMLE-4-options-hf",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    option_choices = {\n        \"A\": doc[\"ending0\"],\n        \"B\": doc[\"ending1\"],\n        \"C\": doc[\"ending2\"],\n        \"D\": doc[\"ending3\"],\n    }\n    answers = \"\".join((f\"{k}. {v}\\n\") for k, v in option_choices.items())\n    return f\"Question: {doc['sent1']}\\n{answers}Answer:\"\n",
+      "doc_to_target": "def doc_to_target(doc) -> int:\n    return doc[\"label\"]\n",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "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
+    },
+    "mmlu_anatomy": {
+      "task": "mmlu_anatomy",
+      "task_alias": "anatomy (mmlu)",
+      "tag": "mmlu_stem_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "anatomy",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_clinical_knowledge": {
+      "task": "mmlu_clinical_knowledge",
+      "task_alias": "clinical_knowledge (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "clinical_knowledge",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_college_biology": {
+      "task": "mmlu_college_biology",
+      "task_alias": "college_biology (mmlu)",
+      "tag": "mmlu_stem_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_biology",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college biology.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_college_medicine": {
+      "task": "mmlu_college_medicine",
+      "task_alias": "college_medicine (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_medicine",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_medical_genetics": {
+      "task": "mmlu_medical_genetics",
+      "task_alias": "medical_genetics (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "medical_genetics",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "mmlu_professional_medicine": {
+      "task": "mmlu_professional_medicine",
+      "task_alias": "professional_medicine (mmlu)",
+      "tag": "mmlu_other_tasks",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "professional_medicine",
+      "dataset_kwargs": {
+        "trust_remote_code": true
+      },
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "pubmedqa": {
+      "task": "pubmedqa",
+      "dataset_path": "bigbio/pubmed_qa",
+      "dataset_name": "pubmed_qa_labeled_fold0_source",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    ctxs = \"\\n\".join(doc[\"CONTEXTS\"])\n    return \"Abstract: {}\\nQuestion: {}\\nAnswer:\".format(\n        ctxs,\n        doc[\"QUESTION\"],\n    )\n",
+      "doc_to_target": "final_decision",
+      "doc_to_choice": [
+        "yes",
+        "no",
+        "maybe"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "medmcqa": "Yaml",
+    "medqa_4options": "Yaml",
+    "mmlu_anatomy": 1.0,
+    "mmlu_clinical_knowledge": 1.0,
+    "mmlu_college_biology": 1.0,
+    "mmlu_college_medicine": 1.0,
+    "mmlu_medical_genetics": 1.0,
+    "mmlu_professional_medicine": 1.0,
+    "pubmedqa": 1.0
+  },
+  "n-shot": {
+    "medmcqa": 0,
+    "medqa_4options": 0,
+    "mmlu_anatomy": 0,
+    "mmlu_clinical_knowledge": 0,
+    "mmlu_college_biology": 0,
+    "mmlu_college_medicine": 0,
+    "mmlu_medical_genetics": 0,
+    "mmlu_professional_medicine": 0,
+    "pubmedqa": 0
+  },
+  "higher_is_better": {
+    "medmcqa": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "medqa_4options": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "mmlu_anatomy": {
+      "acc": true
+    },
+    "mmlu_clinical_knowledge": {
+      "acc": true
+    },
+    "mmlu_college_biology": {
+      "acc": true
+    },
+    "mmlu_college_medicine": {
+      "acc": true
+    },
+    "mmlu_medical_genetics": {
+      "acc": true
+    },
+    "mmlu_professional_medicine": {
+      "acc": true
+    },
+    "multimedqa": {
+      "acc": true,
+      "acc_norm": true
+    },
+    "pubmedqa": {
+      "acc": true
+    }
+  },
+  "n-samples": {
+    "pubmedqa": {
+      "original": 500,
+      "effective": 500
+    },
+    "medmcqa": {
+      "original": 4183,
+      "effective": 4183
+    },
+    "medqa_4options": {
+      "original": 1273,
+      "effective": 1273
+    },
+    "mmlu_anatomy": {
+      "original": 135,
+      "effective": 135
+    },
+    "mmlu_clinical_knowledge": {
+      "original": 265,
+      "effective": 265
+    },
+    "mmlu_college_medicine": {
+      "original": 173,
+      "effective": 173
+    },
+    "mmlu_medical_genetics": {
+      "original": 100,
+      "effective": 100
+    },
+    "mmlu_professional_medicine": {
+      "original": 272,
+      "effective": 272
+    },
+    "mmlu_college_biology": {
+      "original": 144,
+      "effective": 144
+    }
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=checkpoint-5222,parallelize=True,trust_remote_code=True",
+    "model_num_parameters": 7615616512,
+    "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": "6915b12",
+  "date": 1742241195.31737,
+  "pretty_env_info": "PyTorch version: 2.5.1+cu124\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.31.0\nLibc version: glibc-2.35\n\nPython version: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.8.0-40-generic-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: GPU 0: NVIDIA H200\nNvidia driver version: 550.127.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0\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:                        46 bits physical, 57 bits virtual\nByte Order:                           Little Endian\nCPU(s):                               192\nOn-line CPU(s) list:                  0-191\nVendor ID:                            GenuineIntel\nModel name:                           INTEL(R) XEON(R) PLATINUM 8568Y+\nCPU family:                           6\nModel:                                207\nThread(s) per core:                   2\nCore(s) per socket:                   48\nSocket(s):                            2\nStepping:                             2\nCPU max MHz:                          4000.0000\nCPU min MHz:                          800.0000\nBogoMIPS:                             4600.00\nFlags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization:                       VT-x\nL1d cache:                            4.5 MiB (96 instances)\nL1i cache:                            3 MiB (96 instances)\nL2 cache:                             192 MiB (96 instances)\nL3 cache:                             600 MiB (2 instances)\nNUMA node(s):                         2\nNUMA node0 CPU(s):                    0-47,96-143\nNUMA node1 CPU(s):                    48-95,144-191\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:               Not affected\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 BHI_DIS_S\nVulnerability Srbds:                  Not affected\nVulnerability Tsx async abort:        Not affected\n\nVersions of relevant libraries:\n[pip3] galore-torch==1.0\n[pip3] lion-pytorch==0.1.2\n[pip3] numpy==1.26.4\n[pip3] torch==2.5.1\n[pip3] torch-optimi==0.2.1\n[pip3] torchao==0.7.0\n[pip3] triton==3.1.0\n[conda] No relevant packages",
+  "transformers_version": "4.46.3",
+  "upper_git_hash": null,
+  "tokenizer_pad_token": [
+    "<|endoftext|>",
+    "151643"
+  ],
+  "tokenizer_eos_token": [
+    "<|im_end|>",
+    "151645"
+  ],
+  "tokenizer_bos_token": [
+    null,
+    "None"
+  ],
+  "eot_token_id": 151645,
+  "max_length": 32768,
+  "task_hashes": {
+    "pubmedqa": "870d7e5614da5dcad21abd725786223f3f6c5f4b6c35263df2d926eb7705a57a",
+    "medmcqa": "4ef81a02eca79c63cacb070d9b6375e2a463961f416266b281c01bccf94e70ea",
+    "medqa_4options": "2a2e29c3d5c0f042309940f072ce2a0c60def924a527be58650dd55665880fd6",
+    "mmlu_anatomy": "8a394ba6aa4d3366637e72da67c7d4c0286d47cb371a4f4a9814259be8bbe3ad",
+    "mmlu_clinical_knowledge": "839bf7b05724190f7277a957e8b2183a7b4dc74ab9ca72063d10872092a1ea7a",
+    "mmlu_college_medicine": "14529d73333850b8be0fc1d4c102c4500b76434c8c761611be6899af27608455",
+    "mmlu_medical_genetics": "9b736fa6d447dd8f017f7e2dc81e7487f3412a8551075ca312e48db9c4c5e108",
+    "mmlu_professional_medicine": "b1c4eea40bd1d93e49c50cadd35db8bbb96392c40d208ae1ffd6e72c306d757a",
+    "mmlu_college_biology": "d983837a4ac4327e74ff7f131eda1f0c23f6c9f2a1088e3a5162c6ede31605d5"
+  },
+  "model_source": "hf",
+  "model_name": "checkpoint-5222",
+  "model_name_sanitized": "checkpoint-5222",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "fewshot_as_multiturn": false,
+  "chat_template": null,
+  "chat_template_sha": null,
+  "start_time": 8223703.085893983,
+  "end_time": 8223794.175920337,
+  "total_evaluation_time_seconds": "91.0900263544172"
+}