257 lines
10 KiB
Markdown
257 lines
10 KiB
Markdown
---
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language:
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- en
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license: cc-by-nc-4.0
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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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- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
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base_model:
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- mlabonne/NeuralMonarch-7B
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model-index:
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- name: AlphaMonarch-7B
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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: 73.04
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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/AlphaMonarch-7B
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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: 89.18
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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/AlphaMonarch-7B
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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: 64.4
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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/AlphaMonarch-7B
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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: 77.91
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/AlphaMonarch-7B
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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: 84.69
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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=mlabonne/AlphaMonarch-7B
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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: 66.72
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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/AlphaMonarch-7B
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name: Open LLM Leaderboard
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---
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# 👑 AlphaMonarch-7B
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**tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉**
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AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset.
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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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* [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
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* [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B)
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* [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)
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Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets.
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**Try the demo**: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B
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## 🔍 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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If you use SillyTavern, you might want to tweak the inference parameters. Here's what LM Studio uses as a reference: `temp` 0.8, `top_k` 40, `top_p` 0.95, `min_p` 0.05, `repeat_penalty` 1.1.
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It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt.
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## ⚡ Quantized models
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Thanks to [LoneStriker](https://huggingface.co/LoneStriker) for the GPTQ, AWQ, and EXL2 quants.
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* **GGUF**: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF
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* **GPTQ**: https://huggingface.co/LoneStriker/AlphaMonarch-7B-GPTQ
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* **AWQ**: https://huggingface.co/LoneStriker/AlphaMonarch-7B-AWQ
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* **mlx**: https://huggingface.co/mlx-community/AlphaMonarch-7B-mlx
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* **EXL2**:
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* https://huggingface.co/LoneStriker/AlphaMonarch-7B-3.0bpw-h6-exl2
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* https://huggingface.co/LoneStriker/AlphaMonarch-7B-4.0bpw-h6-exl2
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* https://huggingface.co/LoneStriker/AlphaMonarch-7B-5.0bpw-h6-exl2
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* https://huggingface.co/LoneStriker/AlphaMonarch-7B-6.0bpw-h6-exl2
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* https://huggingface.co/LoneStriker/AlphaMonarch-7B-8.0bpw-h6-exl2
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## 🏆 Evaluation
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### Nous
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AlphaMonarch-7B is the best-performing 7B model 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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| [**AlphaMonarch-7B**](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) | **62.74** | **45.37** | **77.01** | **78.39** | **50.2** |
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| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](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) [📄](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) [📄](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) [📄](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) [📄](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) [📄](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) [📄](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) [📄](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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AlphaMonarch-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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### 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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### Open LLM Leaderboard
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AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard:
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## 🌳 Model Family Tree
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "mlabonne/AlphaMonarch-7B"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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