169 lines
7.3 KiB
Markdown
169 lines
7.3 KiB
Markdown
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---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- flemmingmiguel/NeuDist-Ro-7B
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- Blizado/discolm-mfto-7b-german-v0.1
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- ResplendentAI/Flora_DPO_7B
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base_model:
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- flemmingmiguel/NeuDist-Ro-7B
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- Blizado/discolm-mfto-7b-german-v0.1
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- ResplendentAI/Flora_DPO_7B
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license: apache-2.0
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---
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# Spaetzle-v12-7b
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Spaetzle-v12-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B)
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* [Blizado/discolm-mfto-7b-german-v0.1](https://huggingface.co/Blizado/discolm-mfto-7b-german-v0.1)
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* [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B)
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* on the basis of [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser)
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As expected, this is a little bit worse in general English tasks over [cstr/spaetzle-v8-7b](https://huggingface.co/cstr/spaetzle-v8-7b), but a tiny little bit better on German tasks, at least some: e.g. it reaches an EQ-Bench (de)
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score of 64.81, but only
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |69.36|
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|AI2 Reasoning Challenge (25-Shot)|65.96|
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|HellaSwag (10-Shot) |86.16|
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|MMLU (5-Shot) |63.48|
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|TruthfulQA (0-shot) |57.84|
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|Winogrande (5-shot) |80.03|
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|GSM8k (5-shot) |62.70|
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|--------------------------------------------------------------|------:|------:|---------:|-------:|------:|
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|[Spaetzle-v12-7b](https://huggingface.co/cstr/Spaetzle-v12-7b)| 42.64| 74.3| 58.44| 44.44| 54.95|
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### AGIEval
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| Task |Version| Metric |Value| |Stderr|
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|------------------------------|------:|--------|----:|---|-----:|
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|agieval_aqua_rat | 0|acc |24.02|± | 2.69|
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| | |acc_norm|21.65|± | 2.59|
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|agieval_logiqa_en | 0|acc |36.10|± | 1.88|
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| | |acc_norm|37.63|± | 1.90|
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|agieval_lsat_ar | 0|acc |24.35|± | 2.84|
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| | |acc_norm|23.04|± | 2.78|
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|agieval_lsat_lr | 0|acc |48.82|± | 2.22|
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| | |acc_norm|47.25|± | 2.21|
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|agieval_lsat_rc | 0|acc |60.59|± | 2.98|
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| | |acc_norm|57.99|± | 3.01|
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|agieval_sat_en | 0|acc |76.21|± | 2.97|
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| | |acc_norm|74.76|± | 3.03|
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|agieval_sat_en_without_passage| 0|acc |46.60|± | 3.48|
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| | |acc_norm|45.63|± | 3.48|
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|agieval_sat_math | 0|acc |37.27|± | 3.27|
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| | |acc_norm|33.18|± | 3.18|
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Average: 42.64%
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### GPT4All
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| Task |Version| Metric |Value| |Stderr|
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|-------------|------:|--------|----:|---|-----:|
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|arc_challenge| 0|acc |59.13|± | 1.44|
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| | |acc_norm|61.26|± | 1.42|
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|arc_easy | 0|acc |83.67|± | 0.76|
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| | |acc_norm|80.89|± | 0.81|
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|boolq | 1|acc |87.83|± | 0.57|
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|hellaswag | 0|acc |66.45|± | 0.47|
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| | |acc_norm|84.63|± | 0.36|
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|openbookqa | 0|acc |37.40|± | 2.17|
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| | |acc_norm|45.80|± | 2.23|
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|piqa | 0|acc |82.15|± | 0.89|
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| | |acc_norm|83.13|± | 0.87|
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|winogrande | 0|acc |76.56|± | 1.19|
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Average: 74.3%
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### TruthfulQA
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| Task |Version|Metric|Value| |Stderr|
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|-------------|------:|------|----:|---|-----:|
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|truthfulqa_mc| 1|mc1 |42.59|± | 1.73|
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| | |mc2 |58.44|± | 1.58|
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Average: 58.44%
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### Bigbench
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| Task |Version| Metric |Value| |Stderr|
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|------------------------------------------------|------:|---------------------|----:|---|-----:|
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|bigbench_causal_judgement | 0|multiple_choice_grade|55.26|± | 3.62|
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|bigbench_date_understanding | 0|multiple_choice_grade|64.77|± | 2.49|
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|37.60|± | 3.02|
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|bigbench_geometric_shapes | 0|multiple_choice_grade|32.31|± | 2.47|
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| | |exact_str_match |21.45|± | 2.17|
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|31.00|± | 2.07|
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|22.43|± | 1.58|
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|53.00|± | 2.89|
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|bigbench_movie_recommendation | 0|multiple_choice_grade|40.40|± | 2.20|
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|bigbench_navigate | 0|multiple_choice_grade|51.30|± | 1.58|
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|68.50|± | 1.04|
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|bigbench_ruin_names | 0|multiple_choice_grade|48.66|± | 2.36|
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|30.36|± | 1.46|
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|bigbench_snarks | 0|multiple_choice_grade|70.17|± | 3.41|
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|bigbench_sports_understanding | 0|multiple_choice_grade|70.39|± | 1.45|
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|bigbench_temporal_sequences | 0|multiple_choice_grade|31.00|± | 1.46|
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.44|± | 1.16|
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.29|± | 0.92|
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|53.00|± | 2.89|
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Average: 44.44%
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Average score: 54.95%
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Elapsed time: 02:50:51
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## 🧩 Configuration
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```yaml
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models:
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- model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
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# no parameters necessary for base model
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- model: flemmingmiguel/NeuDist-Ro-7B
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parameters:
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density: 0.60
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weight: 0.30
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- model: Blizado/discolm-mfto-7b-german-v0.1
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parameters:
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density: 0.65
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weight: 0.40
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- model: ResplendentAI/Flora_DPO_7B
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parameters:
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density: 0.6
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weight: 0.3
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merge_method: dare_ties
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base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser
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parameters:
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int8_mask: true
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dtype: bfloat16
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random_seed: 0
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tokenizer_source: base
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "cstr/Spaetzle-v12-7b"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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