237 lines
7.1 KiB
Markdown
237 lines
7.1 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- transformers
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datasets:
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- mwitiderrick/AlpacaCode
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base_model: openlm-research/open_llama_3b
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inference: true
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model_type: llama
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prompt_template: '### Instruction:\n
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{prompt}
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### Response:
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'
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created_by: mwitiderrick
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pipeline_tag: text-generation
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model-index:
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- name: mwitiderrick/open_llama_3b_instruct_v_0.2
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results:
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- task:
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type: text-generation
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dataset:
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name: hellaswag
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type: hellaswag
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metrics:
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- type: hellaswag (0-Shot)
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value: 0.6581
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name: hellaswag(0-Shot)
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- task:
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type: text-generation
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dataset:
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name: winogrande
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type: winogrande
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metrics:
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- type: winogrande (0-Shot)
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value: 0.6267
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name: winogrande(0-Shot)
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- task:
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type: text-generation
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dataset:
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name: arc_challenge
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type: arc_challenge
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metrics:
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- type: arc_challenge (0-Shot)
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value: 0.3712
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name: arc_challenge(0-Shot)
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source:
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url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
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name: open_llama_3b_instruct_v_0.2 model card
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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: 41.21
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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=mwitiderrick/open_llama_3b_code_instruct_0.1
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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: 66.96
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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=mwitiderrick/open_llama_3b_code_instruct_0.1
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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: 27.82
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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=mwitiderrick/open_llama_3b_code_instruct_0.1
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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: 35.01
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
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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: 65.43
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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=mwitiderrick/open_llama_3b_code_instruct_0.1
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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: 1.9
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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=mwitiderrick/open_llama_3b_code_instruct_0.1
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name: Open LLM Leaderboard
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---
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# OpenLLaMA Code Instruct: An Open Reproduction of LLaMA
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This is an [OpenLlama model](https://huggingface.co/openlm-research/open_llama_3b) that has been fine-tuned on 1 epoch of the
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[AlpacaCode](https://huggingface.co/datasets/mwitiderrick/AlpacaCode) dataset (122K rows).
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## Prompt Template
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```
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### Instruction:
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{query}
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### Response:
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<Leave new line for model to respond>
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```
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
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tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
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model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
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query = "Write a quick sort algorithm in Python"
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text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
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output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
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print(output[0]['generated_text'])
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"""
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### Instruction:
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write a quick sort algorithm in Python
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### Response:
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def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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else:
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)
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arr = [5,2,4,3,1]
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print(quick_sort(arr))
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"""
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[1, 2, 3, 4, 5]
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"""
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```
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## Metrics
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[Detailed metrics](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_code_instruct_0.1)
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```
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
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|----------|-------|------|-----:|------|-----:|---|-----:|
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|winogrande|Yaml |none | 0|acc |0.6267|± |0.0136|
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|hellaswag|Yaml |none | 0|acc |0.4962|± |0.0050|
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| | |none | 0|acc_norm|0.6581|± |0.0047|
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|arc_challenge|Yaml |none | 0|acc |0.3481|± |0.0139|
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| | |none | 0|acc_norm|0.3712|± |0.0141|
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|truthfulqa|N/A |none | 0|bleu_max | 24.2580|± |0.5985|
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| | |none | 0|bleu_acc | 0.2876|± |0.0003|
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| | |none | 0|bleu_diff | -8.3685|± |0.6065|
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| | |none | 0|rouge1_max | 49.3907|± |0.7350|
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| | |none | 0|rouge1_acc | 0.2558|± |0.0002|
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| | |none | 0|rouge1_diff|-10.6617|± |0.6450|
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| | |none | 0|rouge2_max | 32.4189|± |0.9587|
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| | |none | 0|rouge2_acc | 0.2142|± |0.0002|
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| | |none | 0|rouge2_diff|-12.9903|± |0.9539|
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| | |none | 0|rougeL_max | 46.2337|± |0.7493|
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| | |none | 0|rougeL_acc | 0.2424|± |0.0002|
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| | |none | 0|rougeL_diff|-11.0285|± |0.6576|
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| | |none | 0|acc | 0.3072|± |0.0405|
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```
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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_mwitiderrick__open_llama_3b_code_instruct_0.1)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |39.72|
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|AI2 Reasoning Challenge (25-Shot)|41.21|
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|HellaSwag (10-Shot) |66.96|
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|MMLU (5-Shot) |27.82|
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|TruthfulQA (0-shot) |35.01|
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|Winogrande (5-shot) |65.43|
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|GSM8k (5-shot) | 1.90|
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