287 lines
11 KiB
Markdown
287 lines
11 KiB
Markdown
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---
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license: other
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tags:
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- moe
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- merge
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- mergekit
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- Mistral
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- openchat/openchat-3.5-1210
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- beowolx/CodeNinja-1.0-OpenChat-7B
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- maywell/PiVoT-0.1-Starling-LM-RP
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- WizardLM/WizardMath-7B-V1.1
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license_name: microsoft-research-license
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license_link: https://huggingface.co/WizardLM/WizardMath-7B-V1.1/resolve/main/LICENSE
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model-index:
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- name: Beyonder-4x7B-v2
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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: 68.77
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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/Beyonder-4x7B-v2
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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: 86.8
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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/Beyonder-4x7B-v2
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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: 65.1
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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/Beyonder-4x7B-v2
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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: 60.68
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Beyonder-4x7B-v2
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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: 80.9
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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=mlabonne/Beyonder-4x7B-v2
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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: 71.72
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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/Beyonder-4x7B-v2
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name: Open LLM Leaderboard
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---
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# Beyonder-4x7B-v2
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This model is a Mixture of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
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* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
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* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
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* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
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The recommended context length is 8k.
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## ⚡ Quantized models
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Thanks to TheBloke for the quantized models:
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* **GGUF**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-GGUF
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* **AWQ**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-AWQ
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* **GPTQ**: https://huggingface.co/TheBloke/Beyonder-4x7B-v2-GPTQ
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* **EXL2**: https://huggingface.co/bartowski/Beyonder-4x7B-v2-exl2
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## 🏆 Evaluation
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Beyonder-4x7B-v2 is competitive with Mixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard, while only having 4 experts instead of 8.
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It also displays a significant improvement over the individual experts.
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It also performs very well compared to other models on Nous benchmark suite. It's almost as good as the best Yi-34B fine-tune, which is a much bigger model: 24.2B parameters + only two experts are selected during inference (so ~12B) vs. 34B param.
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|--------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
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|[**Beyonder-4x7B-v2**](https://huggingface.co/shadowml/Beyonder-4x7B-v2)| **45.29**| **75.95**| <u>**60.86**</u>| **46.4**| **57.13**|
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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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|[OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)| 42.75| 72.99| 52.99| 40.94| 52.42|
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|[Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)| 47.79| 74.69| 55.92| 44.84| 55.81|
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|[Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)| <u>50.27</u>| <u>76.00</u>| 60.34| <u>46.69</u>| <u>58.33</u>|
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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 |23.62|± | 2.67|
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| | |acc_norm|23.62|± | 2.67|
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|agieval_logiqa_en | 0|acc |41.47|± | 1.93|
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| | |acc_norm|43.01|± | 1.94|
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|agieval_lsat_ar | 0|acc |23.04|± | 2.78|
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| | |acc_norm|23.48|± | 2.80|
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|agieval_lsat_lr | 0|acc |51.57|± | 2.22|
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| | |acc_norm|52.94|± | 2.21|
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|agieval_lsat_rc | 0|acc |64.31|± | 2.93|
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| | |acc_norm|64.68|± | 2.92|
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|agieval_sat_en | 0|acc |79.13|± | 2.84|
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| | |acc_norm|79.13|± | 2.84|
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|agieval_sat_en_without_passage| 0|acc |43.20|± | 3.46|
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| | |acc_norm|43.20|± | 3.46|
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|agieval_sat_math | 0|acc |34.55|± | 3.21|
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| | |acc_norm|32.27|± | 3.16|
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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|64.51|± | 1.40|
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|arc_easy | 0|acc |85.06|± | 0.73|
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| | |acc_norm|82.45|± | 0.78|
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|boolq | 1|acc |88.35|± | 0.56|
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|hellaswag | 0|acc |68.04|± | 0.47|
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| | |acc_norm|85.12|± | 0.36|
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|openbookqa | 0|acc |37.80|± | 2.17|
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| | |acc_norm|48.60|± | 2.24|
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|piqa | 0|acc |83.08|± | 0.87|
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| | |acc_norm|83.95|± | 0.86|
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|winogrande | 0|acc |78.69|± | 1.15|
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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 |44.55|± | 1.74|
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| | |mc2 |60.86|± | 1.57|
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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|58.95|± | 3.58|
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|bigbench_date_understanding | 0|multiple_choice_grade|66.40|± | 2.46|
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|48.84|± | 3.12|
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|bigbench_geometric_shapes | 0|multiple_choice_grade|22.56|± | 2.21|
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| | |exact_str_match |13.37|± | 1.80|
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.40|± | 2.06|
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.57|± | 1.53|
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|52.00|± | 2.89|
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|bigbench_movie_recommendation | 0|multiple_choice_grade|44.40|± | 2.22|
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|bigbench_navigate | 0|multiple_choice_grade|52.10|± | 1.58|
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|69.75|± | 1.03|
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|bigbench_ruin_names | 0|multiple_choice_grade|55.36|± | 2.35|
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|23.65|± | 1.35|
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|bigbench_snarks | 0|multiple_choice_grade|77.35|± | 3.12|
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|bigbench_sports_understanding | 0|multiple_choice_grade|73.02|± | 1.41|
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|bigbench_temporal_sequences | 0|multiple_choice_grade|46.80|± | 1.58|
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.08|± | 1.17|
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|19.03|± | 0.94|
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|52.00|± | 2.89|
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## 🧩 Configuration
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```yaml
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base_model: mlabonne/Marcoro14-7B-slerp
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experts:
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- source_model: openchat/openchat-3.5-1210
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positive_prompts:
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- "chat"
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- "assistant"
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- "tell me"
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- "explain"
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- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
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positive_prompts:
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- "code"
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- "python"
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- "javascript"
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- "programming"
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- "algorithm"
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- source_model: maywell/PiVoT-0.1-Starling-LM-RP
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positive_prompts:
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- "storywriting"
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- "write"
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- "scene"
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- "story"
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- "character"
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- source_model: WizardLM/WizardMath-7B-V1.1
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positive_prompts:
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- "reason"
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- "math"
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- "mathematics"
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- "solve"
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- "count"
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```
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## 💻 Usage
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Here's a [notebook](https://colab.research.google.com/drive/1ypy8fEAJe9RkNmNQR1BduOzy2Qn6CnMl#scrollTo=myLRfwjZcIyP) to run this model in 4-bit precision using a free T4 GPU on Google Colab.
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```python
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!pip install -qU transformers bitsandbytes 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/Beyonder-4x7B-v2"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
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)
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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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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Output:
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> A Mixture of Experts (ME) is a machine learning technique that combines multiple expert models to make predictions or decisions. Each expert model is specialized in a different aspect of the problem, and their outputs are combined to produce a more accurate and robust solution. This approach allows the model to leverage the strengths of individual experts and compensate for their weaknesses, improving overall performance.
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