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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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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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## 🏆 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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```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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``` |
