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--- |
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license: mit |
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model-index: |
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- name: piccolo-math-2x7b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 69.11 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 87.27 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 63.69 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 63.86 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 79.87 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 70.13 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/piccolo-math-2x7b |
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name: Open LLM Leaderboard |
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--- |
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# Piccolo-math-2x7b |
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**In loving memory of my dog Klaus (Piccolo)** |
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_~ Piccolo (Italian): the little one ~_ |
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# Code Example |
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Inference and Evaluation colab available [here](https://colab.research.google.com/drive/1ZqLNvVvtFHC_4v2CgcMVh7pP9Fvx0SbI?usp=sharing) |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def generate_response(prompt): |
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""" |
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Generate a response from the model based on the input prompt. |
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Args: |
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prompt (str): Prompt for the model. |
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Returns: |
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str: The generated response from the model. |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return response |
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model_id = "macadeliccc/piccolo-math-2x7b" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id,load_in_4bit=True) |
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prompt = "What is the best way to train Cane Corsos?" |
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print("Response:") |
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print(generate_response(prompt), "\n") |
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``` |
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The model is capable of quality code, math, and logical reasoning. Try whatever questions you think of. |
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# Evaluations |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|-------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[piccolo-math-2x7b](https://huggingface.co/macadeliccc/piccolo-math-2x7b)| 43.89| 74.98| 63.96| 44.99| 56.96| |
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### EQ Bench |
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#### Benchmark Complete: |
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+ 2024-01-24 00:00:40 |
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+ Time taken: 183.3 mins |
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+ Prompt Format: Mistral |
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+ Model: macadeliccc/piccolo-math-2x7b |
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+ Score (v2): 70.74 |
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+ Parseable: 167.0 |
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--------------- |
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Batch completed |
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Time taken: 183.3 mins |
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### AGIEval |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------|------:|--------|----:|---|-----:| |
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|agieval_aqua_rat | 0|acc |24.41|± | 2.70| |
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| | |acc_norm|24.80|± | 2.72| |
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|agieval_logiqa_en | 0|acc |35.79|± | 1.88| |
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| | |acc_norm|36.71|± | 1.89| |
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|agieval_lsat_ar | 0|acc |23.48|± | 2.80| |
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| | |acc_norm|23.91|± | 2.82| |
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|agieval_lsat_lr | 0|acc |49.22|± | 2.22| |
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| | |acc_norm|50.00|± | 2.22| |
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|agieval_lsat_rc | 0|acc |63.94|± | 2.93| |
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| | |acc_norm|64.31|± | 2.93| |
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|agieval_sat_en | 0|acc |77.18|± | 2.93| |
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| | |acc_norm|76.70|± | 2.95| |
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|agieval_sat_en_without_passage| 0|acc |45.15|± | 3.48| |
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| | |acc_norm|44.66|± | 3.47| |
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|agieval_sat_math | 0|acc |33.64|± | 3.19| |
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| | |acc_norm|30.00|± | 3.10| |
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Average: 43.89% |
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### GPT4All |
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| Task |Version| Metric |Value| |Stderr| |
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|-------------|------:|--------|----:|---|-----:| |
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|arc_challenge| 0|acc |61.86|± | 1.42| |
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| | |acc_norm|62.88|± | 1.41| |
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|arc_easy | 0|acc |84.34|± | 0.75| |
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| | |acc_norm|80.47|± | 0.81| |
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|boolq | 1|acc |86.88|± | 0.59| |
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|hellaswag | 0|acc |68.56|± | 0.46| |
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| | |acc_norm|85.16|± | 0.35| |
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|openbookqa | 0|acc |37.00|± | 2.16| |
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| | |acc_norm|47.80|± | 2.24| |
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|piqa | 0|acc |82.21|± | 0.89| |
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| | |acc_norm|83.68|± | 0.86| |
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|winogrande | 0|acc |77.98|± | 1.16| |
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Average: 74.98% |
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### TruthfulQA |
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| Task |Version|Metric|Value| |Stderr| |
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|-------------|------:|------|----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |47.37|± | 1.75| |
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| | |mc2 |63.96|± | 1.57| |
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Average: 63.96% |
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### Bigbench |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------------------------|------:|---------------------|----:|---|-----:| |
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|bigbench_causal_judgement | 0|multiple_choice_grade|55.26|± | 3.62| |
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|bigbench_date_understanding | 0|multiple_choice_grade|63.14|± | 2.51| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|42.64|± | 3.08| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|22.84|± | 2.22| |
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| | |exact_str_match | 3.34|± | 0.95| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|36.60|± | 2.16| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|25.57|± | 1.65| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|56.00|± | 2.87| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|42.40|± | 2.21| |
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|bigbench_navigate | 0|multiple_choice_grade|54.70|± | 1.57| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|62.90|± | 1.08| |
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|bigbench_ruin_names | 0|multiple_choice_grade|53.35|± | 2.36| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|24.35|± | 1.36| |
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|bigbench_snarks | 0|multiple_choice_grade|62.43|± | 3.61| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|70.28|± | 1.46| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|41.30|± | 1.56| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.32|± | 1.18| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|17.77|± | 0.91| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|56.00|± | 2.87| |
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Average: 44.99% |
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Average score: 56.96% |
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Elapsed time: 01:51:53 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__piccolo-math-2x7b) |
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| Metric |Value| |
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|Avg. |72.32| |
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|AI2 Reasoning Challenge (25-Shot)|69.11| |
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|HellaSwag (10-Shot) |87.27| |
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|MMLU (5-Shot) |63.69| |
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|TruthfulQA (0-shot) |63.86| |
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|Winogrande (5-shot) |79.87| |
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|GSM8k (5-shot) |70.13| |
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