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Evaluating Meta-Llama-3.1-8B-Instruct
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{
"cells": [
{
"cell_type": "markdown",
"id": "f9742a6b",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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"tags": []
},
"source": [
"# Overview\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "f1305389",
"metadata": {
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"status": "completed"
},
"tags": []
},
"source": [
"This is a sample note about how to evaluate language model which is directly copied from Aisuko's note \"Evaluating language models \""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0530b6f3",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [],
"source": [
"# !pip install -U -q lm-eval==0.4.3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "92dfc0c1",
"metadata": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'lm-evaluation-harness'...\r\n",
"remote: Enumerating objects: 40293, done.\u001b[K\r\n",
"remote: Counting objects: 100% (622/622), done.\u001b[K\r\n",
"remote: Compressing objects: 100% (398/398), done.\u001b[K\r\n",
"remote: Total 40293 (delta 323), reused 486 (delta 223), pack-reused 39671 (from 1)\u001b[K\r\n",
"Receiving objects: 100% (40293/40293), 27.00 MiB | 20.82 MiB/s, done.\r\n",
"Resolving deltas: 100% (28252/28252), done.\r\n",
"/kaggle/working/lm-evaluation-harness\n"
]
}
],
"source": [
"!git clone https://github.com/EleutherAI/lm-evaluation-harness\n",
"%cd lm-evaluation-harness\n",
"!pip install --quiet -e ."
]
},
{
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"execution_count": 3,
"id": "cedee79d",
"metadata": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
"Token is valid (permission: read).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"import os\n",
"from huggingface_hub import login\n",
"from kaggle_secrets import UserSecretsClient\n",
"user_secrets = UserSecretsClient()\n",
"\n",
"os.environ[\"HF_TOKEN\"]=user_secrets.get_secret(\"HUGGINGFACE_TOKEN\")\n",
"\n",
"os.environ[\"WANDB_API_KEY\"]=user_secrets.get_secret(\"WANDB_API_KEY\")\n",
"os.environ[\"WANDB_PROJECT\"] = \"Evaluating allenai Meta-Llama-3.1-8B-Instruct\"\n",
"os.environ[\"WANDB_NAME\"] = \"Meta-Llama-3.1-8B-Instruct\"\n",
"os.environ[\"MODEL_NAME\"] = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
"os.environ[\"DATASET\"] = \"HuggingFaceH4/ultrafeedback_binarized\"\n",
"\n",
"login(os.environ[\"HF_TOKEN\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9c271657",
"metadata": {
"_kg_hide-input": true,
"execution": {
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"shell.execute_reply": "2024-09-08T08:58:16.938683Z"
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"usage: lm_eval [-h] [--model MODEL] [--tasks task1,task2]\r\n",
" [--model_args MODEL_ARGS] [--num_fewshot N]\r\n",
" [--batch_size auto|auto:N|N] [--max_batch_size N]\r\n",
" [--device DEVICE] [--output_path DIR|DIR/file.json]\r\n",
" [--limit N|0<N<1] [--use_cache DIR]\r\n",
" [--cache_requests {true,refresh,delete}] [--check_integrity]\r\n",
" [--write_out] [--log_samples]\r\n",
" [--system_instruction SYSTEM_INSTRUCTION]\r\n",
" [--apply_chat_template [APPLY_CHAT_TEMPLATE]]\r\n",
" [--fewshot_as_multiturn] [--show_config] [--include_path DIR]\r\n",
" [--gen_kwargs GEN_KWARGS]\r\n",
" [--verbosity CRITICAL|ERROR|WARNING|INFO|DEBUG]\r\n",
" [--wandb_args WANDB_ARGS] [--hf_hub_log_args HF_HUB_LOG_ARGS]\r\n",
" [--predict_only] [--seed SEED] [--trust_remote_code]\r\n",
"\r\n",
"options:\r\n",
" -h, --help show this help message and exit\r\n",
" --model MODEL, -m MODEL\r\n",
" Name of model e.g. `hf`\r\n",
" --tasks task1,task2, -t task1,task2\r\n",
" Comma-separated list of task names or task groupings to evaluate on.\r\n",
" To get full list of tasks, use one of the commands `lm-eval --tasks {{list_groups,list_subtasks,list_tags,list}}` to list out all available names for task groupings; only (sub)tasks; tags; or all of the above\r\n",
" --model_args MODEL_ARGS, -a MODEL_ARGS\r\n",
" Comma separated string arguments for model, e.g. `pretrained=EleutherAI/pythia-160m,dtype=float32`\r\n",
" --num_fewshot N, -f N\r\n",
" Number of examples in few-shot context\r\n",
" --batch_size auto|auto:N|N, -b auto|auto:N|N\r\n",
" Acceptable values are 'auto', 'auto:N' or N, where N is an integer. Default 1.\r\n",
" --max_batch_size N Maximal batch size to try with --batch_size auto.\r\n",
" --device DEVICE Device to use (e.g. cuda, cuda:0, cpu).\r\n",
" --output_path DIR|DIR/file.json, -o DIR|DIR/file.json\r\n",
" The path to the output file where the result metrics will be saved. If the path is a directory and log_samples is true, the results will be saved in the directory. Else the parent directory will be used.\r\n",
" --limit N|0<N<1, -L N|0<N<1\r\n",
" Limit the number of examples per task. If <1, limit is a percentage of the total number of examples.\r\n",
" --use_cache DIR, -c DIR\r\n",
" A path to a sqlite db file for caching model responses. `None` if not caching.\r\n",
" --cache_requests {true,refresh,delete}\r\n",
" Speed up evaluation by caching the building of dataset requests. `None` if not caching.\r\n",
" --check_integrity Whether to run the relevant part of the test suite for the tasks.\r\n",
" --write_out, -w Prints the prompt for the first few documents.\r\n",
" --log_samples, -s If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis. Use with --output_path.\r\n",
" --system_instruction SYSTEM_INSTRUCTION\r\n",
" System instruction to be used in the prompt\r\n",
" --apply_chat_template [APPLY_CHAT_TEMPLATE]\r\n",
" If True, apply chat template to the prompt. Providing `--apply_chat_template` without an argument will apply the default chat template to the prompt. To apply a specific template from the available list of templates, provide the template name as an argument. E.g. `--apply_chat_template template_name`\r\n",
" --fewshot_as_multiturn\r\n",
" If True, uses the fewshot as a multi-turn conversation\r\n",
" --show_config If True, shows the the full config of all tasks at the end of the evaluation.\r\n",
" --include_path DIR Additional path to include if there are external tasks to include.\r\n",
" --gen_kwargs GEN_KWARGS\r\n",
" String arguments for model generation on greedy_until tasks, e.g. `temperature=0,top_k=0,top_p=0`.\r\n",
" --verbosity CRITICAL|ERROR|WARNING|INFO|DEBUG, -v CRITICAL|ERROR|WARNING|INFO|DEBUG\r\n",
" Controls the reported logging error level. Set to DEBUG when testing + adding new task configurations for comprehensive log output.\r\n",
" --wandb_args WANDB_ARGS\r\n",
" Comma separated string arguments passed to wandb.init, e.g. `project=lm-eval,job_type=eval\r\n",
" --hf_hub_log_args HF_HUB_LOG_ARGS\r\n",
" Comma separated string arguments passed to Hugging Face Hub's log function, e.g. `hub_results_org=EleutherAI,hub_repo_name=lm-eval-results`\r\n",
" --predict_only, -x Use with --log_samples. Only model outputs will be saved and metrics will not be evaluated.\r\n",
" --seed SEED Set seed for python's random, numpy, torch, and fewshot sampling.\r\n",
" Accepts a comma-separated list of 4 values for python's random, numpy, torch, and fewshot sampling seeds, respectively, or a single integer to set the same seed for all four.\r\n",
" The values are either an integer or 'None' to not set the seed. Default is `0,1234,1234,1234` (for backward compatibility).\r\n",
" E.g. `--seed 0,None,8,52` sets `random.seed(0)`, `torch.manual_seed(8)`, and fewshot sampling seed to 52. Here numpy's seed is not set since the second value is `None`.\r\n",
" E.g, `--seed 42` sets all four seeds to 42.\r\n",
" --trust_remote_code Sets trust_remote_code to True to execute code to create HF Datasets from the Hub\r\n"
]
}
],
"source": [
"!lm_eval --help"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "73fa133a",
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},
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{
"name": "stdout",
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"text": [
"config.json: 100%|█████████████████████████████| 855/855 [00:00<00:00, 5.11MB/s]\r\n",
"tokenizer_config.json: 100%|███████████████| 55.4k/55.4k [00:00<00:00, 10.5MB/s]\r\n",
"tokenizer.json: 100%|██████████████████████| 9.09M/9.09M [00:00<00:00, 22.9MB/s]\r\n",
"special_tokens_map.json: 100%|█████████████████| 296/296 [00:00<00:00, 3.06MB/s]\r\n",
"model.safetensors.index.json: 100%|█████████| 23.9k/23.9k [00:00<00:00, 101MB/s]\r\n",
"Downloading shards: 0%| | 0/4 [00:00<?, ?it/s]\r\n",
"model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00<?, ?B/s]\u001b[A\r\n",
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"Downloading shards: 100%|█████████████████████████| 4/4 [01:03<00:00, 15.99s/it]\r\n",
"Loading checkpoint shards: 75%|█████████████▌ | 3/4 [00:40<00:13, 13.42s/it]\r\n",
"Traceback (most recent call last):\r\n",
" File \"/opt/conda/bin/lm_eval\", line 8, in <module>\r\n",
" sys.exit(cli_evaluate())\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/__main__.py\", line 382, in cli_evaluate\r\n",
" results = evaluator.simple_evaluate(\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/utils.py\", line 397, in _wrapper\r\n",
" return fn(*args, **kwargs)\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/evaluator.py\", line 201, in simple_evaluate\r\n",
" lm = lm_eval.api.registry.get_model(model).create_from_arg_string(\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/api/model.py\", line 147, in create_from_arg_string\r\n",
" return cls(**args, **args2)\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/models/huggingface.py\", line 183, in __init__\r\n",
" self._create_model(\r\n",
" File \"/kaggle/working/lm-evaluation-harness/lm_eval/models/huggingface.py\", line 562, in _create_model\r\n",
" self._model = self.AUTO_MODEL_CLASS.from_pretrained(\r\n",
" File \"/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py\", line 564, in from_pretrained\r\n",
" return model_class.from_pretrained(\r\n",
" File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py\", line 3941, in from_pretrained\r\n",
" ) = cls._load_pretrained_model(\r\n",
" File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py\", line 4415, in _load_pretrained_model\r\n",
" new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(\r\n",
" File \"/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py\", line 936, in _load_state_dict_into_meta_model\r\n",
" set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)\r\n",
" File \"/opt/conda/lib/python3.10/site-packages/accelerate/utils/modeling.py\", line 416, in set_module_tensor_to_device\r\n",
" new_value = value.to(device)\r\n",
"torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1002.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 660.12 MiB is free. Process 3193 has 14.09 GiB memory in use. Of the allocated memory 13.87 GiB is allocated by PyTorch, and 129.52 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\r\n"
]
}
],
"source": [
"!lm_eval --model hf \\\n",
" --model_args pretrained=${MODEL_NAME} \\\n",
" --tasks mmlu \\\n",
" --device cuda:0 \\\n",
" --num_fewshot 0 \\\n",
" --batch_size 1 \\\n",
" --output_path results \\\n",
" --use_cache True\\\n",
" --log_samples \\\n",
" --limit 2 \\\n",
"# --hf_hub_log_args hub_results_org=aisuko,hub_repo_name=eval-smolLM-135M,push_results_to_hub=True,push_samples_to_hub=True,public_repo=False"
]
},
{
"cell_type": "markdown",
"id": "98bf9704",
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"papermill": {
"duration": 0.046313,
"end_time": "2024-09-08T09:00:28.640704",
"exception": false,
"start_time": "2024-09-08T09:00:28.594391",
"status": "completed"
},
"tags": []
},
"source": [
"# Related issues\n",
"\n",
"* https://github.com/EleutherAI/lm-evaluation-harness/issues/2263"
]
}
],
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