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 |