| | --- |
| | tags: |
| | - merge |
| | - mergekit |
| | - lazymergekit |
| | - flemmingmiguel/NeuDist-Ro-7B |
| | - Blizado/discolm-mfto-7b-german-v0.1 |
| | - ResplendentAI/Flora_DPO_7B |
| | base_model: |
| | - flemmingmiguel/NeuDist-Ro-7B |
| | - Blizado/discolm-mfto-7b-german-v0.1 |
| | - ResplendentAI/Flora_DPO_7B |
| | license: cc-by-sa-4.0 |
| | --- |
| | |
| | # Spaetzle-v12-7b |
| |
|
| | Spaetzle-v12-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
| | * [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B) |
| | * [Blizado/discolm-mfto-7b-german-v0.1](https://huggingface.co/Blizado/discolm-mfto-7b-german-v0.1) |
| | * [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B) |
| | * on the basis of [mayflowergmbh/Wiedervereinigung-7b-dpo-laser](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo-laser) |
| |
|
| | 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) |
| | score of 64.81, but only |
| |
|
| | | Metric |Value| |
| | |---------------------------------|----:| |
| | |Avg. |69.36| |
| | |AI2 Reasoning Challenge (25-Shot)|65.96| |
| | |HellaSwag (10-Shot) |86.16| |
| | |MMLU (5-Shot) |63.48| |
| | |TruthfulQA (0-shot) |57.84| |
| | |Winogrande (5-shot) |80.03| |
| | |GSM8k (5-shot) |62.70| |
| |
|
| |
|
| | | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
| | |--------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
| | |[Spaetzle-v12-7b](https://huggingface.co/cstr/Spaetzle-v12-7b)| 42.64| 74.3| 58.44| 44.44| 54.95| |
| |
|
| | ### AGIEval |
| | | Task |Version| Metric |Value| |Stderr| |
| | |------------------------------|------:|--------|----:|---|-----:| |
| | |agieval_aqua_rat | 0|acc |24.02|± | 2.69| |
| | | | |acc_norm|21.65|± | 2.59| |
| | |agieval_logiqa_en | 0|acc |36.10|± | 1.88| |
| | | | |acc_norm|37.63|± | 1.90| |
| | |agieval_lsat_ar | 0|acc |24.35|± | 2.84| |
| | | | |acc_norm|23.04|± | 2.78| |
| | |agieval_lsat_lr | 0|acc |48.82|± | 2.22| |
| | | | |acc_norm|47.25|± | 2.21| |
| | |agieval_lsat_rc | 0|acc |60.59|± | 2.98| |
| | | | |acc_norm|57.99|± | 3.01| |
| | |agieval_sat_en | 0|acc |76.21|± | 2.97| |
| | | | |acc_norm|74.76|± | 3.03| |
| | |agieval_sat_en_without_passage| 0|acc |46.60|± | 3.48| |
| | | | |acc_norm|45.63|± | 3.48| |
| | |agieval_sat_math | 0|acc |37.27|± | 3.27| |
| | | | |acc_norm|33.18|± | 3.18| |
| |
|
| | Average: 42.64% |
| |
|
| | ### GPT4All |
| | | Task |Version| Metric |Value| |Stderr| |
| | |-------------|------:|--------|----:|---|-----:| |
| | |arc_challenge| 0|acc |59.13|± | 1.44| |
| | | | |acc_norm|61.26|± | 1.42| |
| | |arc_easy | 0|acc |83.67|± | 0.76| |
| | | | |acc_norm|80.89|± | 0.81| |
| | |boolq | 1|acc |87.83|± | 0.57| |
| | |hellaswag | 0|acc |66.45|± | 0.47| |
| | | | |acc_norm|84.63|± | 0.36| |
| | |openbookqa | 0|acc |37.40|± | 2.17| |
| | | | |acc_norm|45.80|± | 2.23| |
| | |piqa | 0|acc |82.15|± | 0.89| |
| | | | |acc_norm|83.13|± | 0.87| |
| | |winogrande | 0|acc |76.56|± | 1.19| |
| | |
| | Average: 74.3% |
| | |
| | ### TruthfulQA |
| | | Task |Version|Metric|Value| |Stderr| |
| | |-------------|------:|------|----:|---|-----:| |
| | |truthfulqa_mc| 1|mc1 |42.59|± | 1.73| |
| | | | |mc2 |58.44|± | 1.58| |
| |
|
| | Average: 58.44% |
| |
|
| | ### Bigbench |
| | | Task |Version| Metric |Value| |Stderr| |
| | |------------------------------------------------|------:|---------------------|----:|---|-----:| |
| | |bigbench_causal_judgement | 0|multiple_choice_grade|55.26|± | 3.62| |
| | |bigbench_date_understanding | 0|multiple_choice_grade|64.77|± | 2.49| |
| | |bigbench_disambiguation_qa | 0|multiple_choice_grade|37.60|± | 3.02| |
| | |bigbench_geometric_shapes | 0|multiple_choice_grade|32.31|± | 2.47| |
| | | | |exact_str_match |21.45|± | 2.17| |
| | |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|31.00|± | 2.07| |
| | |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|22.43|± | 1.58| |
| | |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|53.00|± | 2.89| |
| | |bigbench_movie_recommendation | 0|multiple_choice_grade|40.40|± | 2.20| |
| | |bigbench_navigate | 0|multiple_choice_grade|51.30|± | 1.58| |
| | |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|68.50|± | 1.04| |
| | |bigbench_ruin_names | 0|multiple_choice_grade|48.66|± | 2.36| |
| | |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|30.36|± | 1.46| |
| | |bigbench_snarks | 0|multiple_choice_grade|70.17|± | 3.41| |
| | |bigbench_sports_understanding | 0|multiple_choice_grade|70.39|± | 1.45| |
| | |bigbench_temporal_sequences | 0|multiple_choice_grade|31.00|± | 1.46| |
| | |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|21.44|± | 1.16| |
| | |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.29|± | 0.92| |
| | |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|53.00|± | 2.89| |
| | |
| | Average: 44.44% |
| | |
| | Average score: 54.95% |
| | |
| | Elapsed time: 02:50:51 |
| | |
| | ## 🧩 Configuration |
| | |
| | ```yaml |
| | models: |
| | - model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser |
| | # no parameters necessary for base model |
| | - model: flemmingmiguel/NeuDist-Ro-7B |
| | parameters: |
| | density: 0.60 |
| | weight: 0.30 |
| | - model: Blizado/discolm-mfto-7b-german-v0.1 |
| | parameters: |
| | density: 0.65 |
| | weight: 0.40 |
| | - model: ResplendentAI/Flora_DPO_7B |
| | parameters: |
| | density: 0.6 |
| | weight: 0.3 |
| | merge_method: dare_ties |
| | base_model: mayflowergmbh/Wiedervereinigung-7b-dpo-laser |
| | 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-v12-7b" |
| | 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"]) |
| | ``` |
