--- 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 drawing 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)](https://arxiv.org/abs/2312.06281) 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](https://arxiv.org/abs/2311.03099) 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](https://github.com/EQ-bench/EQ-Bench), outscoring larger models like [abacusai/Smaug-72B-v0.1](https://huggingface.co/abacusai/Smaug-72B-v0.1) and [cognitivecomputations/dolphin-2.2-70b](https://huggingface.co/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)](https://arxiv.org/abs/2306.05685), outscoring Chat-GPT 3.5 and Claude v1. This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/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](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) * [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) * [cognitivecomputations/WestLake-7B-v2-laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) * [senseable/garten2-7b](https://huggingface.co/senseable/garten2-7b) ### 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](https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1)| 44.99| 76.93| 68.04| 47.71| 59.42| |[mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) | 45.37| 77 | 78.39| 50.2 | 62.74| |[berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/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)](https://arxiv.org/abs/2306.05685) | [(Paper)](https://arxiv.org/abs/2312.06281) [Leaderboard](https://eqbench.com/) |