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---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- AIDC-ai-business/Marcoroni-7B-v3
- EmbeddedLLM/Mistral-7B-Merge-14-v0.1
---
# Marcoro14-7B-slerp
This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit):
* [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3)
* [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1)
## 🏆 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
```yaml
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
```python
!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.
|