README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - yleo/EmertonMonarch-7B
base_model:
  - yleo/EmertonMonarch-7B

Spaetzle-v31-7b

Spaetzle-v31-7b is a merge of the following models using LazyMergekit:

• yleo/EmertonMonarch-7B
• cstr/spaetzle-v8-7b

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Spaetzle-v31-7b	46.23	76.6	69.58	46.79	59.8

AGIEval

Task	Version	Metric	Value		Stderr
agieval_aqua_rat	0	acc	28.74	±	2.85
		acc_norm	27.56	±	2.81
agieval_logiqa_en	0	acc	39.63	±	1.92
		acc_norm	40.25	±	1.92
agieval_lsat_ar	0	acc	24.35	±	2.84
		acc_norm	24.35	±	2.84
agieval_lsat_lr	0	acc	54.31	±	2.21
		acc_norm	54.12	±	2.21
agieval_lsat_rc	0	acc	65.80	±	2.90
		acc_norm	66.54	±	2.88
agieval_sat_en	0	acc	79.13	±	2.84
		acc_norm	79.61	±	2.81
agieval_sat_en_without_passage	0	acc	46.12	±	3.48
		acc_norm	45.15	±	3.48
agieval_sat_math	0	acc	35.00	±	3.22
		acc_norm	32.27	±	3.16

Average: 46.23%

GPT4All

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	64.76	±	1.40
		acc_norm	66.89	±	1.38
arc_easy	0	acc	86.66	±	0.70
		acc_norm	82.83	±	0.77
boolq	1	acc	87.80	±	0.57
hellaswag	0	acc	67.43	±	0.47
		acc_norm	85.85	±	0.35
openbookqa	0	acc	38.00	±	2.17
		acc_norm	48.80	±	2.24
piqa	0	acc	83.57	±	0.86
		acc_norm	84.71	±	0.84
winogrande	0	acc	79.32	±	1.14

Average: 76.6%

TruthfulQA

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	53.37	±	1.75
		mc2	69.58	±	1.48

Average: 69.58%

Bigbench

Task	Version	Metric	Value		Stderr
bigbench_causal_judgement	0	multiple_choice_grade	56.84	±	3.60
bigbench_date_understanding	0	multiple_choice_grade	66.94	±	2.45
bigbench_disambiguation_qa	0	multiple_choice_grade	44.57	±	3.10
bigbench_geometric_shapes	0	multiple_choice_grade	21.17	±	2.16
		exact_str_match	0.28	±	0.28
bigbench_logical_deduction_five_objects	0	multiple_choice_grade	31.80	±	2.08
bigbench_logical_deduction_seven_objects	0	multiple_choice_grade	22.57	±	1.58
bigbench_logical_deduction_three_objects	0	multiple_choice_grade	56.00	±	2.87
bigbench_movie_recommendation	0	multiple_choice_grade	45.40	±	2.23
bigbench_navigate	0	multiple_choice_grade	52.80	±	1.58
bigbench_reasoning_about_colored_objects	0	multiple_choice_grade	70.65	±	1.02
bigbench_ruin_names	0	multiple_choice_grade	50.67	±	2.36
bigbench_salient_translation_error_detection	0	multiple_choice_grade	30.66	±	1.46
bigbench_snarks	0	multiple_choice_grade	71.27	±	3.37
bigbench_sports_understanding	0	multiple_choice_grade	74.34	±	1.39
bigbench_temporal_sequences	0	multiple_choice_grade	49.80	±	1.58
bigbench_tracking_shuffled_objects_five_objects	0	multiple_choice_grade	22.16	±	1.18
bigbench_tracking_shuffled_objects_seven_objects	0	multiple_choice_grade	18.57	±	0.93
bigbench_tracking_shuffled_objects_three_objects	0	multiple_choice_grade	56.00	±	2.87

Average: 46.79%

Average score: 59.8%

Elapsed time: 02:09:50

🧩 Configuration

models:
  - model: cstr/spaetzle-v8-7b
    # no parameters necessary for base model
  - model: yleo/EmertonMonarch-7B
    parameters:
      density: 0.60
      weight: 0.3
merge_method: dare_ties
base_model: cstr/spaetzle-v8-7b
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/Spaetzle-v31-7b"
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"])