File size: 10,367 Bytes
4eab9ea
70fc1a9
4eab9ea
 
 
d1e4097
 
 
4936f46
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4eab9ea
 
38aba7d
 
4eab9ea
 
 
 
777b2c7
4eab9ea
38aba7d
 
ef2d8a7
38aba7d
 
 
 
 
8f4b077
38aba7d
 
 
 
 
8f4b077
38aba7d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8f4b077
38aba7d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8f4b077
38aba7d
 
8f4b077
38aba7d
 
 
 
8f4b077
38aba7d
 
8f4b077
38aba7d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8f4b077
38aba7d
 
 
 
4eab9ea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38aba7d
 
 
 
 
 
 
 
 
 
 
5aa70ec
38aba7d
 
 
 
 
 
 
 
 
 
 
 
 
 
cc0ad73
 
 
4936f46
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
---
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
---

![](https://i.imgur.com/FSKtmRc.png)

# 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):

![](https://i.imgur.com/5XUuP7g.png)

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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__Marcoro14-7B-slerp)

|             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|