--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - flemmingmiguel/MBX-7B-v3 - Gille/StrangeMerges_9-7B-dare_ties base_model: - flemmingmiguel/MBX-7B-v3 - Gille/StrangeMerges_9-7B-dare_ties model-index: - name: StrangeMerges_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: 72.35 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_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: 88.3 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_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: 64.87 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_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: 69.49 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_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: 83.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_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: 70.13 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_10-7B-slerp name: Open LLM Leaderboard --- # StrangeMerges_10-7B-slerp StrangeMerges_10-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [Gille/StrangeMerges_9-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_9-7B-dare_ties) ## 🧩 Configuration ```yaml slices: - sources: - model: flemmingmiguel/MBX-7B-v3 layer_range: [0, 32] - model: Gille/StrangeMerges_9-7B-dare_ties layer_range: [0, 32] merge_method: slerp base_model: Gille/StrangeMerges_9-7B-dare_ties parameters: t: - filter: self_attn value: [0.1, 0.5, 0.3, 0.7, 0.9] - filter: mlp value: [0.9, 0.5, 0.7, 0.3, 0.1] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/StrangeMerges_10-7B-slerp" 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"]) ``` # [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_Gille__StrangeMerges_10-7B-slerp) | Metric |Value| |---------------------------------|----:| |Avg. |74.77| |AI2 Reasoning Challenge (25-Shot)|72.35| |HellaSwag (10-Shot) |88.30| |MMLU (5-Shot) |64.87| |TruthfulQA (0-shot) |69.49| |Winogrande (5-shot) |83.50| |GSM8k (5-shot) |70.13|