Name	Quant method	Size
Starling_Monarch_Westlake_Garten-7B-v0.1.Q2_K.gguf	Q2_K	2.53GB
Starling_Monarch_Westlake_Garten-7B-v0.1.IQ3_XS.gguf	IQ3_XS	2.81GB
Starling_Monarch_Westlake_Garten-7B-v0.1.IQ3_S.gguf	IQ3_S	2.96GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
Starling_Monarch_Westlake_Garten-7B-v0.1.IQ3_M.gguf	IQ3_M	3.06GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q3_K.gguf	Q3_K	3.28GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
Starling_Monarch_Westlake_Garten-7B-v0.1.IQ4_XS.gguf	IQ4_XS	3.67GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q4_0.gguf	Q4_0	3.83GB
Starling_Monarch_Westlake_Garten-7B-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q4_K.gguf	Q4_K	4.07GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q4_1.gguf	Q4_1	4.24GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q5_0.gguf	Q5_0	4.65GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q5_K_S.gguf	Q5_K_S	4.65GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q5_K.gguf	Q5_K	4.78GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q5_1.gguf	Q5_1	5.07GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q6_K.gguf	Q6_K	5.53GB
Starling_Monarch_Westlake_Garten-7B-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

base_model:

mistralai/Mistral-7B-v0.1
berkeley-nest/Starling-LM-7B-alpha
mlabonne/AlphaMonarch-7B
cognitivecomputations/WestLake-7B-v2-laser
senseable/garten2-7b

library_name: transformers tags:

mergekit
merge license: cc-by-nc-4.0

model-index:

name: Starling_Monarch_Westlake_Garten-7B-v0.1 results:
- task: type: text-generation name: Text Generation dataset: name: EQ-Bench type: eq-bench config: EQ-Bench split: v2.1 args: num_few_shot: 3 metrics:
  - type: acc_norm value: 80.01 name: self-reported source: url: https://github.com/EQ-bench/EQ-Bench name: EQ-Bench v2.1
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 71.76 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 88.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 65.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 67.92 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 82.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 71.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 name: 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:

Configuration

The following YAML configuration was used to produce this model:

yaml

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

RichardErkhov/giraffe176_-_Starling_Monarch_Westlake_Garten-7B-v0.1-gguf