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Beyonder-4x7B-v2

This model is a Mixture of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:

The recommended context length is 8k.

⚡ Quantized models

Thanks to TheBloke for the quantized models:

🏆 Evaluation

Beyonder-4x7B-v2 is competitive with Mixtral-8x7B-Instruct-v0.1 on the Open LLM Leaderboard, while only having 4 experts instead of 8.

It also displays a significant improvement over the individual experts.

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.

Model AGIEval GPT4All TruthfulQA Bigbench Average
Beyonder-4x7B-v2 45.29 75.95 60.86 46.4 57.13
NeuralHermes-2.5-Mistral-7B 43.67 73.24 55.37 41.76 53.51
OpenHermes-2.5-Mistral-7B 42.75 72.99 52.99 40.94 52.42
Nous-Hermes-2-SOLAR-10.7B 47.79 74.69 55.92 44.84 55.81
Nous-Hermes-2-Yi-34B 50.27 76.00 60.34 46.69 58.33

AGIEval

Task Version Metric Value Stderr
agieval_aqua_rat 0 acc 23.62 ± 2.67
acc_norm 23.62 ± 2.67
agieval_logiqa_en 0 acc 41.47 ± 1.93
acc_norm 43.01 ± 1.94
agieval_lsat_ar 0 acc 23.04 ± 2.78
acc_norm 23.48 ± 2.80
agieval_lsat_lr 0 acc 51.57 ± 2.22
acc_norm 52.94 ± 2.21
agieval_lsat_rc 0 acc 64.31 ± 2.93
acc_norm 64.68 ± 2.92
agieval_sat_en 0 acc 79.13 ± 2.84
acc_norm 79.13 ± 2.84
agieval_sat_en_without_passage 0 acc 43.20 ± 3.46
acc_norm 43.20 ± 3.46
agieval_sat_math 0 acc 34.55 ± 3.21
acc_norm 32.27 ± 3.16

GPT4All

Task Version Metric Value Stderr
arc_challenge 0 acc 61.86 ± 1.42
acc_norm 64.51 ± 1.40
arc_easy 0 acc 85.06 ± 0.73
acc_norm 82.45 ± 0.78
boolq 1 acc 88.35 ± 0.56
hellaswag 0 acc 68.04 ± 0.47
acc_norm 85.12 ± 0.36
openbookqa 0 acc 37.80 ± 2.17
acc_norm 48.60 ± 2.24
piqa 0 acc 83.08 ± 0.87
acc_norm 83.95 ± 0.86
winogrande 0 acc 78.69 ± 1.15

TruthfulQA

Task Version Metric Value Stderr
truthfulqa_mc 1 mc1 44.55 ± 1.74
mc2 60.86 ± 1.57

Bigbench

Task Version Metric Value Stderr
bigbench_causal_judgement 0 multiple_choice_grade 58.95 ± 3.58
bigbench_date_understanding 0 multiple_choice_grade 66.40 ± 2.46
bigbench_disambiguation_qa 0 multiple_choice_grade 48.84 ± 3.12
bigbench_geometric_shapes 0 multiple_choice_grade 22.56 ± 2.21
exact_str_match 13.37 ± 1.80
bigbench_logical_deduction_five_objects 0 multiple_choice_grade 30.40 ± 2.06
bigbench_logical_deduction_seven_objects 0 multiple_choice_grade 20.57 ± 1.53
bigbench_logical_deduction_three_objects 0 multiple_choice_grade 52.00 ± 2.89
bigbench_movie_recommendation 0 multiple_choice_grade 44.40 ± 2.22
bigbench_navigate 0 multiple_choice_grade 52.10 ± 1.58
bigbench_reasoning_about_colored_objects 0 multiple_choice_grade 69.75 ± 1.03
bigbench_ruin_names 0 multiple_choice_grade 55.36 ± 2.35
bigbench_salient_translation_error_detection 0 multiple_choice_grade 23.65 ± 1.35
bigbench_snarks 0 multiple_choice_grade 77.35 ± 3.12
bigbench_sports_understanding 0 multiple_choice_grade 73.02 ± 1.41
bigbench_temporal_sequences 0 multiple_choice_grade 46.80 ± 1.58
bigbench_tracking_shuffled_objects_five_objects 0 multiple_choice_grade 22.08 ± 1.17
bigbench_tracking_shuffled_objects_seven_objects 0 multiple_choice_grade 19.03 ± 0.94
bigbench_tracking_shuffled_objects_three_objects 0 multiple_choice_grade 52.00 ± 2.89

🧩 Configuration

base_model: mlabonne/Marcoro14-7B-slerp
experts:
  - source_model: openchat/openchat-3.5-1210
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
  - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: maywell/PiVoT-0.1-Starling-LM-RP
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
  - source_model: WizardLM/WizardMath-7B-V1.1
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"

💻 Usage

Here's a notebook to run this model in 4-bit precision using a free T4 GPU on Google Colab.

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Beyonder-4x7B-v2"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Output:

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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