Starling_Monarch_Westlake_Garten-7B-v0.1/README.md

base_model, library_name, tags, license, model-index

base_model	library_name	tags	license	model-index
mistralai/Mistral-7B-v0.1 berkeley-nest/Starling-LM-7B-alpha mlabonne/AlphaMonarch-7B cognitivecomputations/WestLake-7B-v2-laser senseable/garten2-7b	transformers	mergekit merge	cc-by-nc-4.0	name results Starling_Monarch_Westlake_Garten-7B-v0.1 task dataset metrics source type name text-generation Text Generation name type config split args EQ-Bench eq-bench EQ-Bench v2.1 num_few_shot 3 type value name acc_norm 80.01 self-reported url name https://github.com/EQ-bench/EQ-Bench EQ-Bench v2.1 task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 71.76 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 88.15 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 65.07 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 67.92 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 82.16 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 71.95 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 Open LLM Leaderboard

Starling_Monarch_Westlake_Garten-7B-v0.1

After experimenting with density for a previous merge (containing similar models), I decided to experiment with weight gradients. My thought that was that if the merge was done with care and attention, I'd be able to create something greater than the sum of its parts. Hoping that, through a merge of really good models, I'd be able to create something greater than the sum of its parts.

I came across the EQ-Bench Benchmark (Paper) as part of my earlier testing. It is a very light and quick benchmark that yields powerful insights into how well the model performs in emotional intelligence related prompts. As part of this process, I tried to figure out if there was a way to determine an optimal set of gradient weights that would lead to the most successful merge as measured against EQ-Bench. At first, my goal was to simply exceed WestLake-7B, but then I kept pushing to see what I could come up with. Too late in the process, I learned that dare_ties has a random element to it. Valuable information for next time, I guess. After concluding that project, I began collecting more data, this time setting a specified seed in mergekit for reproducibility. As I was collecting data, I hit the goal I had set for myself. This model is not a result of the above work but is the genesis of how this model came to be.

I present, Starling_Monarch_Westlake_Garten-7B-v0.1, the only 7B model to score > 80 on the EQ-Bench v2.1 benchmark found here, outscoring larger models like abacusai/Smaug-72B-v0.1 and cognitivecomputations/dolphin-2.2-70b

It also surpasses its components in the GSM8K benchmark, with a score of 71.95. I'll be looking to bring out more logic and emotion in the next evolution of this model.

It also earned 8.109 on MT-Bench(paper), outscoring Chat-GPT 3.5 and Claude v1.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base. The seed for this merge is 176

Models Merged

The following models were included in the merge:

• berkeley-nest/Starling-LM-7B-alpha
• mlabonne/AlphaMonarch-7B
• cognitivecomputations/WestLake-7B-v2-laser
• senseable/garten2-7b

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model

  - model: cognitivecomputations/WestLake-7B-v2-laser
    parameters:
      density: 0.58
      weight:  [0.3877, 0.1636, 0.186, 0.0502]



  - model: senseable/garten2-7b
    parameters:
      density: 0.58
      weight:  [0.234, 0.2423, 0.2148, 0.2775]



  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      density: 0.58
      weight:  [0.1593, 0.1573, 0.1693, 0.3413]



  - model: mlabonne/AlphaMonarch-7B
    parameters:
      density: 0.58
      weight:  [0.219, 0.4368, 0.4299, 0.331]



merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

Table of Benchmarks

Open LLM Leaderboard

	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	74.9	71.76	88.15	65.07	67.92	84.53	71.95
mlabonne/AlphaMonarch-7B	75.99	73.04	89.18	64.4	77.91	84.69	66.72
senseable/WestLake-7B-v2	74.68	73.04	88.65	64.71	67.06	86.98	67.63
berkeley-nest/Starling-LM-7B-alpha	67.13	63.82	84.9	63.64	46.39	80.58	62.4
senseable/garten2-7b	72.65	69.37	87.54	65.44	59.5	84.69	69.37

Yet Another LLM Leaderboard benchmarks

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	44.99	76.93	68.04	47.71	59.42
mlabonne/AlphaMonarch-7B	45.37	77	78.39	50.2	62.74
berkeley-nest/Starling-LM-7B-alpha	42.06	72.72	47.33	42.53	51.16

Misc. Benchmarks

	MT-Bench	EQ-Bench v2.1
giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1	8.109375	80.01 (3 Shot, ChatML, ooba)
mlabonne/AlphaMonarch-7B	8.23750	76.08
senseable/WestLake-7B-v2	X	78.7
berkeley-nest/Starling-LM-7B-alpha	8.09	68.69 (1 Shot, ChatML, ooba)
senseable/garten2-7b	X	75.03
claude-v1	7.900000	76.83
gpt-3.5-turbo	7.943750	71.74
	(Paper)	(Paper) Leaderboard