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Renaming Model moreh/MoMo-70B-LoRA-V1.4 to moreh/MoMo-72B-LoRA-V1.4 (#1)
Browse files- Renaming Model moreh/MoMo-70B-LoRA-V1.4 to moreh/MoMo-72B-LoRA-V1.4 (4f85e016734efb653c41f7369bc8efa219f78d1d)
Co-authored-by: leejunhyeok <leejunhyeok@users.noreply.huggingface.co>
- README.md +9 -9
- results_2024-01-05T09-27-55.373220.json +1 -1
README.md
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---
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pretty_name: Evaluation run of moreh/MoMo-
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dataset_summary: "Dataset automatically created during the evaluation run of model\
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\ [moreh/MoMo-
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\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
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\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
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\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
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\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
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\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
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\nTo load the details from a run, you can for instance do the following:\n```python\n\
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from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_moreh__MoMo-
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,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
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These are the [latest results from run 2024-01-05T09:27:55.373220](https://huggingface.co/datasets/open-llm-leaderboard/details_moreh__MoMo-
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\ that their might be results for other tasks in the repos if successive evals didn't\
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\ cover the same tasks. You find each in the results and the \"latest\" split for\
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\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.767579679155859,\n\
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: {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.010370455551343345\n\
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\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7020470053070508,\n \
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\ \"acc_stderr\": 0.012597932232914529\n }\n}\n```"
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repo_url: https://huggingface.co/moreh/MoMo-
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leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
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point_of_contact: clementine@hf.co
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configs:
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- results_2024-01-05T09-27-55.373220.parquet
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---
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# Dataset Card for Evaluation run of moreh/MoMo-
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<!-- Provide a quick summary of the dataset. -->
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Dataset automatically created during the evaluation run of model [moreh/MoMo-
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The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
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To load the details from a run, you can for instance do the following:
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```python
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from datasets import load_dataset
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data = load_dataset("open-llm-leaderboard/details_moreh__MoMo-
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"harness_winogrande_5",
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split="train")
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```
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## Latest results
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These are the [latest results from run 2024-01-05T09:27:55.373220](https://huggingface.co/datasets/open-llm-leaderboard/details_moreh__MoMo-
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```python
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{
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---
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pretty_name: Evaluation run of moreh/MoMo-72B-LoRA-V1.4
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dataset_summary: "Dataset automatically created during the evaluation run of model\
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\ [moreh/MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) on\
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\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
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\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
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\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
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\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
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\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
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\nTo load the details from a run, you can for instance do the following:\n```python\n\
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from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_moreh__MoMo-72B-LoRA-V1.4\"\
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,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
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These are the [latest results from run 2024-01-05T09:27:55.373220](https://huggingface.co/datasets/open-llm-leaderboard/details_moreh__MoMo-72B-LoRA-V1.4/blob/main/results_2024-01-05T09-27-55.373220.json)(note\
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\ that their might be results for other tasks in the repos if successive evals didn't\
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\ cover the same tasks. You find each in the results and the \"latest\" split for\
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\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.767579679155859,\n\
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: {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.010370455551343345\n\
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\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7020470053070508,\n \
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\ \"acc_stderr\": 0.012597932232914529\n }\n}\n```"
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repo_url: https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4
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leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
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point_of_contact: clementine@hf.co
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configs:
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- results_2024-01-05T09-27-55.373220.parquet
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---
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# Dataset Card for Evaluation run of moreh/MoMo-72B-LoRA-V1.4
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<!-- Provide a quick summary of the dataset. -->
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Dataset automatically created during the evaluation run of model [moreh/MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
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To load the details from a run, you can for instance do the following:
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```python
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from datasets import load_dataset
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data = load_dataset("open-llm-leaderboard/details_moreh__MoMo-72B-LoRA-V1.4",
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"harness_winogrande_5",
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split="train")
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```
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## Latest results
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These are the [latest results from run 2024-01-05T09:27:55.373220](https://huggingface.co/datasets/open-llm-leaderboard/details_moreh__MoMo-72B-LoRA-V1.4/blob/main/results_2024-01-05T09-27-55.373220.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
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```python
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{
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results_2024-01-05T09-27-55.373220.json
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"start_time": 1325739.429145952,
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"end_time": 1363714.335384634,
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"total_evaluation_time_secondes": "37974.9062386821",
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-
"model_name": "moreh/MoMo-
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"model_sha": "66bf25995056155b5d0796f7c0981e243bdd48f3",
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"model_dtype": "torch.bfloat16",
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"model_size": "135.9 GB"
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"start_time": 1325739.429145952,
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"end_time": 1363714.335384634,
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"total_evaluation_time_secondes": "37974.9062386821",
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"model_name": "moreh/MoMo-72B-LoRA-V1.4",
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"model_sha": "66bf25995056155b5d0796f7c0981e243bdd48f3",
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"model_dtype": "torch.bfloat16",
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"model_size": "135.9 GB"
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