Marcoro14-7B-slerp / README.md
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metadata
license: cc-by-nc-4.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - AIDC-ai-business/Marcoroni-7B-v3
  - EmbeddedLLM/Mistral-7B-Merge-14-v0.1
model-index:
  - name: Marcoro14-7B-slerp
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 69.8
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 87.13
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 65.11
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 63.54
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 81.61
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 70.89
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Marcoro14-7B-slerp
          name: Open LLM Leaderboard

Marcoro14-7B-slerp

This model is a merge of the following models made with mergekit:

🏆 Evaluation

Marcoro14-7B-slerp is the best-performing 7B LLM on the Open LLM Leaderboard (rank 1 below is 9B):

I also evaluated it using Nous' benchmark suite and obtained the following results:

Model AGIEval GPT4ALL TruthfulQA Bigbench Average
Marcoro14-7B-slerp 44.66 76.24 64.15 45.64 57.67
OpenHermes-2.5-Mistral-7B 43.07 73.12 53.04 40.96 52.57
Change +1.59 +3.12 +11.11 +4.68 +5.1

AGIEval

Task Version Metric Value Stderr
agieval_aqua_rat 0 acc 26.38 ± 2.77
acc_norm 24.41 ± 2.70
agieval_logiqa_en 0 acc 38.25 ± 1.91
acc_norm 39.32 ± 1.92
agieval_lsat_ar 0 acc 24.35 ± 2.84
acc_norm 25.22 ± 2.87
agieval_lsat_lr 0 acc 50.00 ± 2.22
acc_norm 50.59 ± 2.22
agieval_lsat_rc 0 acc 62.83 ± 2.95
acc_norm 62.08 ± 2.96
agieval_sat_en 0 acc 79.61 ± 2.81
acc_norm 79.61 ± 2.81
agieval_sat_en_without_passage 0 acc 45.15 ± 3.48
acc_norm 45.63 ± 3.48
agieval_sat_math 0 acc 33.18 ± 3.18
acc_norm 30.45 ± 3.11

Average: 44.66%

GPT4ALL

Task Version Metric Value Stderr
arc_challenge 0 acc 63.91 ± 1.40
acc_norm 64.93 ± 1.39
arc_easy 0 acc 86.07 ± 0.71
acc_norm 83.75 ± 0.76
boolq 1 acc 88.56 ± 0.56
hellaswag 0 acc 67.31 ± 0.47
acc_norm 85.28 ± 0.35
openbookqa 0 acc 36.40 ± 2.15
acc_norm 48.20 ± 2.24
piqa 0 acc 82.59 ± 0.88
acc_norm 84.39 ± 0.85
winogrande 0 acc 78.53 ± 1.15

Average: 76.24%

TruthfulQA

Task Version Metric Value Stderr
truthfulqa_mc 1 mc1 46.88 ± 1.75
mc2 64.15 ± 1.52

Average: 64.15%

Bigbench

Task Version Metric Value Stderr
bigbench_causal_judgement 0 multiple_choice_grade 56.32 ± 3.61
bigbench_date_understanding 0 multiple_choice_grade 66.40 ± 2.46
bigbench_disambiguation_qa 0 multiple_choice_grade 45.35 ± 3.11
bigbench_geometric_shapes 0 multiple_choice_grade 20.33 ± 2.13
exact_str_match 4.74 ± 1.12
bigbench_logical_deduction_five_objects 0 multiple_choice_grade 30.00 ± 2.05
bigbench_logical_deduction_seven_objects 0 multiple_choice_grade 21.43 ± 1.55
bigbench_logical_deduction_three_objects 0 multiple_choice_grade 52.33 ± 2.89
bigbench_movie_recommendation 0 multiple_choice_grade 39.20 ± 2.19
bigbench_navigate 0 multiple_choice_grade 53.90 ± 1.58
bigbench_reasoning_about_colored_objects 0 multiple_choice_grade 72.15 ± 1.00
bigbench_ruin_names 0 multiple_choice_grade 52.46 ± 2.36
bigbench_salient_translation_error_detection 0 multiple_choice_grade 25.75 ± 1.38
bigbench_snarks 0 multiple_choice_grade 72.38 ± 3.33
bigbench_sports_understanding 0 multiple_choice_grade 73.63 ± 1.40
bigbench_temporal_sequences 0 multiple_choice_grade 45.70 ± 1.58
bigbench_tracking_shuffled_objects_five_objects 0 multiple_choice_grade 23.44 ± 1.20
bigbench_tracking_shuffled_objects_seven_objects 0 multiple_choice_grade 18.51 ± 0.93
bigbench_tracking_shuffled_objects_three_objects 0 multiple_choice_grade 52.33 ± 2.89

Average: 45.64%

Average score: 57.67%

🧩 Configuration

slices:
  - sources:
      - model: AIDC-ai-business/Marcoroni-7B-v3
        layer_range: [0, 32]
      - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1
        layer_range: [0, 32]
merge_method: slerp
base_model: AIDC-ai-business/Marcoroni-7B-v3
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Marcoro14-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

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 large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data. It's designed to understand and generate human-like language, making predictions on what words or phrases might come next in a sentence or document. These models use complex algorithms and neural network architectures to learn from the data and improve their performance over time. Some well-known large language models include GPT-3 from OpenAI and BERT from Google.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 73.01
AI2 Reasoning Challenge (25-Shot) 69.80
HellaSwag (10-Shot) 87.13
MMLU (5-Shot) 65.11
TruthfulQA (0-shot) 63.54
Winogrande (5-shot) 81.61
GSM8k (5-shot) 70.89