269 lines
11 KiB
Markdown
269 lines
11 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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tags:
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- mistral
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- instruct
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- finetune
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- chatml
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- gpt4
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- synthetic data
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- distillation
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- dpo
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- rlhf
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- laser
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datasets:
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- mlabonne/chatml_dpo_pairs
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base_model: teknium/OpenHermes-2.5-Mistral-7B
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model-index:
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- name: NeuralHermes-2.5-Mistral-7B-laser
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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: 66.38
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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=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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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: 85.09
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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=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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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: 63.43
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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=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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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: 54.95
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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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: 78.14
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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=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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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: 55.72
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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=mlabonne/NeuralHermes-2.5-Mistral-7B-laser
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name: Open LLM Leaderboard
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---
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<center><img src="https://i.imgur.com/gUlEJuU.jpeg"></center>
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# NeuralHermes 2.5 - Mistral 7B - LASER
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This is an experimental LASER version of NeuralHermes using [laserRMT](https://github.com/cognitivecomputations/laserRMT), based on [this paper](https://arxiv.org/pdf/2312.13558.pdf).
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|------------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
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|[NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)| 43.54| 73.44| 55.26| 42.24| 53.62|
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|[NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) | 43.67| 73.24| 55.37| 41.76| 53.51|
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Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024.
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NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
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It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
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The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
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## Results
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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 |21.26|± | 2.57|
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| | |acc_norm|22.83|± | 2.64|
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|agieval_logiqa_en | 0|acc |39.32|± | 1.92|
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| | |acc_norm|40.71|± | 1.93|
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|agieval_lsat_ar | 0|acc |25.65|± | 2.89|
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| | |acc_norm|25.65|± | 2.89|
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|agieval_lsat_lr | 0|acc |48.82|± | 2.22|
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| | |acc_norm|50.00|± | 2.22|
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|agieval_lsat_rc | 0|acc |58.36|± | 3.01|
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| | |acc_norm|57.25|± | 3.02|
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|agieval_sat_en | 0|acc |74.27|± | 3.05|
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| | |acc_norm|73.30|± | 3.09|
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|agieval_sat_en_without_passage| 0|acc |43.69|± | 3.46|
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| | |acc_norm|42.23|± | 3.45|
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|agieval_sat_math | 0|acc |37.27|± | 3.27|
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| | |acc_norm|36.36|± | 3.25|
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Average: 43.54%
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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 |57.76|± | 1.44|
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| | |acc_norm|60.32|± | 1.43|
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|arc_easy | 0|acc |83.84|± | 0.76|
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| | |acc_norm|81.10|± | 0.80|
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|boolq | 1|acc |86.70|± | 0.59|
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|hellaswag | 0|acc |63.15|± | 0.48|
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| | |acc_norm|82.55|± | 0.38|
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|openbookqa | 0|acc |34.40|± | 2.13|
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| | |acc_norm|45.20|± | 2.23|
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|piqa | 0|acc |81.94|± | 0.90|
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| | |acc_norm|82.97|± | 0.88|
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|winogrande | 0|acc |75.22|± | 1.21|
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Average: 73.44%
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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 |37.70|± | 1.70|
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| | |mc2 |55.26|± | 1.52|
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Average: 55.26%
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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|53.16|± | 3.63|
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|bigbench_date_understanding | 0|multiple_choice_grade|65.31|± | 2.48|
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|34.11|± | 2.96|
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|bigbench_geometric_shapes | 0|multiple_choice_grade|27.02|± | 2.35|
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| | |exact_str_match | 0.28|± | 0.28|
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|27.80|± | 2.01|
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|19.86|± | 1.51|
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|48.33|± | 2.89|
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|bigbench_movie_recommendation | 0|multiple_choice_grade|41.40|± | 2.20|
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|bigbench_navigate | 0|multiple_choice_grade|50.00|± | 1.58|
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|65.00|± | 1.07|
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|bigbench_ruin_names | 0|multiple_choice_grade|46.21|± | 2.36|
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|27.25|± | 1.41|
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|bigbench_snarks | 0|multiple_choice_grade|70.72|± | 3.39|
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|bigbench_sports_understanding | 0|multiple_choice_grade|65.72|± | 1.51|
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|bigbench_temporal_sequences | 0|multiple_choice_grade|30.40|± | 1.46|
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.56|± | 1.18|
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|17.09|± | 0.90|
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|48.33|± | 2.89|
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Average: 42.24%
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Average score: 53.62%
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## Usage
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You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
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You can also run this model using the following code:
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```python
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import transformers
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from transformers import AutoTokenizer
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# Format prompt
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message = [
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{"role": "system", "content": "You are a helpful assistant chatbot."},
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{"role": "user", "content": "What is a Large Language Model?"}
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]
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tokenizer = AutoTokenizer.from_pretrained(new_model)
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
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# Create pipeline
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pipeline = transformers.pipeline(
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"text-generation",
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model="mlabonne/NeuralHermes-2.5-Mistral-7B-laser",
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tokenizer=tokenizer
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)
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# Generate text
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sequences = pipeline(
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prompt,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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num_return_sequences=1,
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max_length=200,
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)
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print(sequences[0]['generated_text'])
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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_mlabonne__NeuralHermes-2.5-Mistral-7B-laser)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |67.29|
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|AI2 Reasoning Challenge (25-Shot)|66.38|
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|HellaSwag (10-Shot) |85.09|
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|MMLU (5-Shot) |63.43|
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|TruthfulQA (0-shot) |54.95|
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|Winogrande (5-shot) |78.14|
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|GSM8k (5-shot) |55.72|
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