--- language: - ko license: cc-by-nc-4.0 tags: - merge - lazymergekit - LDCC/LDCC-SOLAR-10.7B - upstage/SOLAR-10.7B-Instruct-v1.0 base_model: - LDCC/LDCC-SOLAR-10.7B - upstage/SOLAR-10.7B-Instruct-v1.0 model-index: - name: SOLAR-10.7B-slerp results: - 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: 68.17 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 86.91 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 66.73 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 67.42 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 84.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 62.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp name: Open LLM Leaderboard --- # SOLAR-10.7B-slerp SOLAR-10.7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) * [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) ## Github [https://github.com/sunjin7725/SOLAR-10.7b-slerp](https://github.com/sunjin7725/SOLAR-10.7b-slerp) ## Benchmark ### Open-Ko-LLM-Leaderboard | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 | | ------: | -----: | -----------: | ------: | ------------: | --------------: | | 56.93 | 53.58 | 62.03 | 53.31 | 57.16 | 58.56 | ## How to use ```Python import torch from transformers import AutoModelForCausalLM, AutoTokenizer repo = 'SJ-Donald/SOLAR-10.7B-slerp' tokenizer = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) ``` ## 🧩 Configuration ```yaml slices: - sources: - model: LDCC/LDCC-SOLAR-10.7B layer_range: [0, 48] - model: upstage/SOLAR-10.7B-Instruct-v1.0 layer_range: [0, 48] merge_method: slerp base_model: upstage/SOLAR-10.7B-Instruct-v1.0 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 tokenizer_source: union dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SJ-Donald__SOLAR-10.7B-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |72.58| |AI2 Reasoning Challenge (25-Shot)|68.17| |HellaSwag (10-Shot) |86.91| |MMLU (5-Shot) |66.73| |TruthfulQA (0-shot) |67.42| |Winogrande (5-shot) |84.06| |GSM8k (5-shot) |62.17|