File size: 9,492 Bytes
b315dd9 cf0fab9 b315dd9 4fc4f67 fa97af3 b315dd9 3e7c276 55b2118 885447c 55b2118 3c3d879 b315dd9 cb0fc74 885447c 09ac4a1 b315dd9 d9c918d b315dd9 4fc4f67 2853d47 09ac4a1 885447c b315dd9 |
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 |
---
tags:
- merge
- mergekit
- lazymergekit
language:
- de
- en
base_model:
- abideen/AlphaMonarch-dora
- mayflowergmbh/Wiedervereinigung-7b-dpo
- flemmingmiguel/NeuDist-Ro-7B
- ResplendentAI/Flora_DPO_7B
- yleo/EmertonMonarch-7B
- occiglot/occiglot-7b-de-en-instruct
- OpenPipe/mistral-ft-optimized-1227
- DiscoResearch/DiscoLM_German_7b_v1
- LeoLM/leo-mistral-hessianai-7b
- DRXD1000/Phoenix
- VAGOsolutions/SauerkrautLM-7b-v1-mistral
- malteos/hermeo-7b
- FelixChao/WestSeverus-7B-DPO-v2
- cognitivecomputations/openchat-3.5-0106-laser
license: cc-by-nc-4.0
---
# Spaetzle-v69-7b
This is a progressive (mostly dare-ties, but also slerp) merge with the intention of a suitable compromise for English and German local tasks.
There is also a 4q_k_m quantized [GGUF](https://huggingface.co/cstr/Spaetzle-v69-7b-GGUF).
It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage).
## Evaluation
Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria.
Nevertheless, they are not too bad:
It achieves (running quantized) in
- German EQ Bench: Score (v2_de): 62.59 (Parseable: 171.0).
- English EQ Bench: Score (v2): 76.43 (Parseable: 171.0).
[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_cstr__Spaetzle-v69-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.87|
|AI2 Reasoning Challenge (25-Shot)|69.54|
|HellaSwag (10-Shot) |86.77|
|MMLU (5-Shot) |64.63|
|TruthfulQA (0-shot) |65.61|
|Winogrande (5-shot) |81.93|
|GSM8k (5-shot) |68.76|
Nous benchmark results:
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Spaetzle-v69-7b](https://huggingface.co/cstr/Spaetzle-v69-7b)| 44.48| 75.84| 66.15| 46.59| 58.27|
### AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |25.98|± | 2.76|
| | |acc_norm|23.62|± | 2.67|
|agieval_logiqa_en | 0|acc |39.78|± | 1.92|
| | |acc_norm|39.48|± | 1.92|
|agieval_lsat_ar | 0|acc |23.48|± | 2.80|
| | |acc_norm|23.91|± | 2.82|
|agieval_lsat_lr | 0|acc |50.00|± | 2.22|
| | |acc_norm|51.76|± | 2.21|
|agieval_lsat_rc | 0|acc |63.94|± | 2.93|
| | |acc_norm|64.31|± | 2.93|
|agieval_sat_en | 0|acc |76.70|± | 2.95|
| | |acc_norm|77.67|± | 2.91|
|agieval_sat_en_without_passage| 0|acc |46.12|± | 3.48|
| | |acc_norm|44.17|± | 3.47|
|agieval_sat_math | 0|acc |34.09|± | 3.20|
| | |acc_norm|30.91|± | 3.12|
Average: 44.48%
### GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |63.23|± | 1.41|
| | |acc_norm|64.16|± | 1.40|
|arc_easy | 0|acc |85.90|± | 0.71|
| | |acc_norm|82.49|± | 0.78|
|boolq | 1|acc |87.80|± | 0.57|
|hellaswag | 0|acc |67.05|± | 0.47|
| | |acc_norm|85.19|± | 0.35|
|openbookqa | 0|acc |38.40|± | 2.18|
| | |acc_norm|48.40|± | 2.24|
|piqa | 0|acc |82.75|± | 0.88|
| | |acc_norm|84.28|± | 0.85|
|winogrande | 0|acc |78.53|± | 1.15|
Average: 75.84%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |50.67|± | 1.75|
| | |mc2 |66.15|± | 1.48|
Average: 66.15%
### Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|56.84|± | 3.60|
|bigbench_date_understanding | 0|multiple_choice_grade|66.67|± | 2.46|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|40.70|± | 3.06|
|bigbench_geometric_shapes | 0|multiple_choice_grade|24.79|± | 2.28|
| | |exact_str_match |10.58|± | 1.63|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|31.00|± | 2.07|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|23.00|± | 1.59|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|58.00|± | 2.85|
|bigbench_movie_recommendation | 0|multiple_choice_grade|45.80|± | 2.23|
|bigbench_navigate | 0|multiple_choice_grade|52.10|± | 1.58|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|69.55|± | 1.03|
|bigbench_ruin_names | 0|multiple_choice_grade|48.88|± | 2.36|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|30.96|± | 1.46|
|bigbench_snarks | 0|multiple_choice_grade|73.48|± | 3.29|
|bigbench_sports_understanding | 0|multiple_choice_grade|74.14|± | 1.40|
|bigbench_temporal_sequences | 0|multiple_choice_grade|42.70|± | 1.56|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|23.60|± | 1.20|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.40|± | 0.93|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|58.00|± | 2.85|
Average: 46.59%
Average score: 58.27%
## 🧩 Merge Configuration
Spaetzle-v69-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora)
* [cstr/Spaetzle-v68-7b](https://huggingface.co/cstr/Spaetzle-v68-7b)
The merge tree in total involves the following original models:
- [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora)
- [mayflowergmbh/Wiedervereinigung-7b-dpo](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo)
- [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B)
- [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B)
- [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B)
- [occiglot/occiglot-7b-de-en-instruct](https://huggingface.co/occiglot/occiglot-7b-de-en-instruct)
- [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
- [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1)
- [LeoLM/leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b)
- [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix)
- [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral)
- [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b)
- [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
- [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser)
For this last merge:
```yaml
models:
- model: cstr/Spaetzle-v68-7b
# no parameters necessary for base model
- model: abideen/AlphaMonarch-dora
parameters:
density: 0.60
weight: 0.30
merge_method: dare_ties
base_model: cstr/Spaetzle-v68-7b
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "cstr/Spaetzle-v69-7b"
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"])
``` |