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README.md
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tags:
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- merge
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- lazymergekit
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dataset:
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- mlabonne/truthy-dpo-v0.1
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- mlabonne/distilabel-intel-orca-dpo-pairs
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# π NeuralMonarch-7B
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**Update 14/02/24: NeuralMonarch-7B is the new best-performing 7B model on Nous' benchmark suite! π**
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NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets.
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It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets.
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## π Applications
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This model uses a context window of 8k.
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Compared to other 7B models, it
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## β‘ Quantized models
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## π Evaluation
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| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
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|---|---:|---:|---:|---:|---:|
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| [**NeuralMonarch-7B**](https://huggingface.co/mlabonne/NeuralMonarch-7B) [π](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) | **62.73** | **45.31** | **76.99** | **78.35** | **50.28** |
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| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [π](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) | 62.68 | 45.48 | 77.07 | 78.04 | 50.14 |
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| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [π](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
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| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [π](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
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| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [π](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) | 60.25 | 46.06 | 76.77 | 70.32 | 47.86 |
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| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [π](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) | 62.5 | 44.57 | 76.34 | 79.81 | 49.27 |
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| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [π](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) | 62.5 | 44.61 | 76.33 | 79.8 | 49.24 |
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## π» Usage
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```python
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tags:
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- merge
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- lazymergekit
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- dpo
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- rlhf
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dataset:
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- mlabonne/truthy-dpo-v0.1
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- mlabonne/distilabel-intel-orca-dpo-pairs
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# π NeuralMonarch-7B
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NeuralMonarch-7B is a DPO fine-tuned of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B/) using the [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) preference datasets.
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It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), and [Argilla](https://huggingface.co/argilla) for the preference datasets.
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**Try the demo**: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat
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## π Applications
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This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).
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Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B).
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## β‘ Quantized models
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## π Evaluation
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### Nous
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NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
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| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
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|---|---:|---:|---:|---:|---:|
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| [**NeuralMonarch-7B**](https://huggingface.co/mlabonne/NeuralMonarch-7B) [π](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) | **62.73** | **45.31** | **76.99** | **78.35** | **50.28** |
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| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [π](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 |
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| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [π](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) | 62.68 | 45.48 | 77.07 | 78.04 | 50.14 |
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| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [π](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
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| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [π](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
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| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [π](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) | 60.25 | 46.06 | 76.77 | 70.32 | 47.86 |
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| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [π](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) | 62.3 | 45.85 | 77.26 | 76.06 | 50.03 |
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| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [π](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) | 62.5 | 44.57 | 76.34 | 79.81 | 49.27 |
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| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [π](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) | 62.5 | 44.61 | 76.33 | 79.8 | 49.24 |
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### EQ-bench
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NeuralMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations.
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png)
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### Open LLM Leaderboard
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NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard.
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### MT-Bench
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```
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########## First turn ##########
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score
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model turn
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gpt-4 1 8.95625
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OmniBeagle-7B 1 8.31250
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AlphaMonarch-7B 1 8.23750
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claude-v1 1 8.15000
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NeuralMonarch-7B 1 8.09375
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gpt-3.5-turbo 1 8.07500
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claude-instant-v1 1 7.80000
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########## Second turn ##########
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score
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model turn
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gpt-4 2 9.025000
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claude-instant-v1 2 8.012658
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OmniBeagle-7B 2 7.837500
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gpt-3.5-turbo 2 7.812500
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claude-v1 2 7.650000
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AlphaMonarch-7B 2 7.618750
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NeuralMonarch-7B 2 7.375000
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########## Average ##########
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score
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model
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gpt-4 8.990625
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OmniBeagle-7B 8.075000
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gpt-3.5-turbo 7.943750
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AlphaMonarch-7B 7.928125
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claude-instant-v1 7.905660
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claude-v1 7.900000
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NeuralMonarch-7B 7.734375
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NeuralBeagle14-7B 7.628125
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```
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## π» Usage
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```python
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